Fetal anatomical structure segmentation in ultrasound images is essential for biometric measurement and disease diagnosis. However, current methods focus on a specific plane or a few structures, whereas obstetricians diagnose by considering multiple structures from different planes. In addition, existing methods struggle with segmenting fuzzy regions, which leads to performance degradation. We propose a real-time segmentation method called Class-aware Multi-structure Instance Segmentation (CMIS), designed to segment 19 key structures in 3 fetal brain planes to support brain-disease diagnosis. We extract instance information and generate class-aware attention for each class instead of dense instances to save computing resources and provide more informative details. Then we implement cross-layer and multi-scale fusion to obtain detailed prototypes. Finally, we fuse global attention with local prototypes cropped by boxes to generate masks and randomly perturb the boxes during training to enhance robustness. Moreover, we propose a new fuzzy region-based constraint loss to address the challenge of structures with varying scales and fuzzy boundaries. Extensive experiments on a fetal brain dataset demonstrate that CMIS outperforms 13 competing baselines, with an mDice of 83.41$\pm$0.03% at 37 FPS. CMIS also excels in external experiments on a fetal heart ultrasound dataset, achieving a mDice of 85.73$\pm$0.02% . These results demonstrate the effectiveness of CMIS in segmenting complex anatomical structures in ultrasound and its potential for real-time clinical applications. CMIS is limited to 2D normal standard planes ($\geq$19 weeks). Thus, its generalization to abnormal cases and broader datasets remains to be investigated.

ABSTRACT Dragon boat racing, a popular aquatic folklore team sport, is traditionally held during the Dragon Boat Festival. Inspired by this event, we propose a novel human‐based meta‐heuristic algorithm called dragon boat optimization (DBO) in this paper. It models the unique behaviours of each crew member on the dragon boat during the race by introducing social psychology mechanisms (social loafing, social incentive). Throughout this process, the focus is on the interaction and collaboration among the crew members, as well as their decision‐making in various situations. During each iteration, DBO implements different state updating strategies. By accurately modelling the crew's behaviour and employing adaptive state update strategies, DBO consistently achieves high optimization performance, as validated by comprehensive testing on 29 benchmark functions and 2 structural design problems. Experimental results indicate that DBO outperforms 7 and 16 state‐of‐the‐art meta‐heuristic algorithms across these test functions and problems, respectively.

Amid the ongoing pandemic, reducing reliance on manual diagnostic procedures has become crucial. In this light, deep neural networks (DNNs) have demonstrated substantial progress in coronavirus disease 2019 (COVID-19) detection. However, when exposed to ‘toxic samples’—imprecise or uncertain data such as outliers, noisy or mislabeled entries, that negatively impact the training process—existing methods cannot effectively protect the training convergence from the overshoot phenomenon. This situation would slow the training convergence. Additionally, current diagnostic models necessitate substantial re-tuning time to adapt to new virus strains or to handle data from different platforms. This research focuses on the parameter updates design and propose a highly controllable and stable DNN for COVID-19 detection. By exploiting the past, current and future changes of the gradient in a fuzzy logic manner, and taking into account the cross-coupling effect between the gradient and its rate of change, we achieve dynamic, high-precision control on parameter updates in DNN optimization to reach a stable status at a faster convergence rate. In each iteration, the current learning rate adjusts itself to the current optimal value within the fuzzy neighboring region. Potentially hereditary module sequentially transfers the trained knowledge between estimators while updating the fuzzy universe range based on the calculated contraction–expansion factors. Consequently, our proposed algorithm alleviates the overshoot suffered by toxic samples, meanwhile effectively enhancing the model robustness, resource-efficiency, flexibility, adaptability, and compatibility. When tested on popular DNN architectures , it yields up to 47.18% acceleration with promising accuracy on four public datasets. Extensive experiments prove the effectiveness of our method in comparison to state-of-the-art optimizers and diagnosis systems, facilitating the real-life demands for COVID-19 detection.

Nasopharyngeal carcinoma (NPC) is a malignant tumor that originates from the back of the nasal canal from above the soft palate to the upper larynx. Because the nasopharyngeal location is deeply hidden, it is often difficult for a single imaging means to clarify its complex adjacency. In addition, there exist some differences and uncertainties in its clinical manifestations. Although two-view fuzzy classifiers can effectively tap into the nasopharyngeal location for hidden information and exhibit good classification performance, existing fuzzy reasoning for predicting whether or not a nasopharyngeal cancer often stems from the inability to reuse the one-sided rules. Therefore, a novel two-view mutual rectification and knowledge mergence Takagi-Sugeno-Kang fuzzy classifier (TVRM-TFC) is proposed here to address the challenge of using imaging means to fine-tune the organ tissues. Firstly, Kullback-Leibler divergence (KLIC) is used to select important features from various imaging sections (i.e., pieces of knowledge). Secondly, the interpretable zero-order Takagi-Sugeno-Kang (TSK) fuzzy classifier is used as the basic training unit to simultaneously obtain satisfactory accuracies and concise linguistic interpretability. Thirdly, from the perspective of both imaging means and the organ, this study fine-tunes the information required for decision-making between different imaging means, so that the complementary advantages of the different views may improve the decision-making information and thus increase decision accuracies. Finally, the perspective of imaging technology and the organ are merged to capture decision-making knowledge. These decision-making advantages from different views are organically integrated to compensate information and further optimize the decision-making information. The merits of the proposed classifier are demonstrated through comparative experimental analysis on CT and MRI data.

The rapid diagnosis of COVID-19 has become a pressing issue due to the strain the outbreak has placed on the healthcare system. This article aims to investigate the rapid and accurate diagnosis of COVID-19. This paper first introduces several widely used COVID-19 diagnostic techniques: rRT-PCR has excellent specificity and sensitivity, making it one of the most trustworthy ways to find the SARS-CoV-2 virus. Diagnostics based on X-rays are frequently employed as an adjunctive method. CT-based diagnosis can offer comprehensive details regarding lung health. It then highlights how machine learning combined with X-ray and CT images can be used to diagnose COVID-19. This approach can improve the accuracy and efficiency of detecting and evaluating the disease, helping healthcare professionals make decisions. Several standard machine learning methods are introduced, including supervised, unsupervised, and semi-supervised learning. Lastly, it forecasts machine learning development in the healthcare sector.

Objectives: Accurate extraction of regions of interest (ROI) with variable shapes and scales is one of the primary challenges in medical image segmentation . Current U-based networks mostly aggregate multi-stage encoding outputs as an improved multi-scale skip connection. Although this design has been proven to provide scale diversity and contextual integrity, there remain several intuitive limits: (i) the encoding outputs are resampled to the same size simply, which destruct the fine-grained information. The advantages of utilization of multiple scales are insufficient. (ii) Certain redundant information proportional to the feature dimension size is introduced and causes multi-stage interference. And (iii) the precision of information delivery relies on the up-sampling and down-sampling layers, but guidance on maintaining consistency in feature locations and trends between them is lacking. Methods: To improve these situations, this paper proposed a U-based CNN network named HAD-Net, by assembling a new hyper-scale shifted aggregating module (HSAM) paradigm and progressive reusing attention (PRA) for skip connections, as well as employing a novel pair of dual-branch parameter-free sampling layers, i.e. max-diagonal pooling (MDP) and max-diagonal un-pooling (MDUP). That is, the aggregating scheme additionally combines five subregions with certain offsets in the shallower stage. Since the lower scale-down ratios of subregions enrich scales and fine-grain context. Then, the attention scheme contains a partial-to-global channel attention (PGCA) and a multi-scale reusing spatial attention (MRSA), it builds reusing connections internally and adjusts the focus on more useful dimensions. Finally, MDP and MDUP are explored in pairs to improve texture delivery and feature consistency, enhancing information retention and avoiding positional confusion. Results: Compared to state-of-the-art networks, HAD-Net has achieved comparable and even better performances with Dice of 90.13%, 81.51%, and 75.43% for each class on BraTS20, 89.59% Dice and 98.56% AUC on Kvasir-SEG, as well as 82.17% Dice and 98.05% AUC on DRIVE. Conclusions: The scheme of HSAM+PRA+MDP+MDUP has been proven to be a remarkable improvement and leaves room for further research.

The rapid proliferation of medical imaging technologies presents a significant challenge for cross-domain adaptive image detection, as lesion representations can vary dramatically across technologies. To address this issue, we draw inspiration from large language models to propose EAFP-Med, an efficient adaptive feature processing module based on prompts for medical image detection. EAFP-Med incorporates a prompt-driven dynamic parameter update mechanism, empowering it to extract cross-domain multi-scale lesion features from medical images of diverse modalities adaptively. This exceptional flexibility liberates it from the constraints of any particular imaging technique, fostering great adaptability. Furthermore, EAFP-Med can also serve as a feature preprocessing module connected to any model front-end to enhance the lesion features in input images. Moreover, we propose a novel adaptive disease detection model named EAFP-Med ST, which utilizes the Swin Transformer V2 – Tiny (SwinV2-T) as its backbone and connects it to EAFP-Med. We have compared our method to nine state-of-the-art methods. Experimental results show that the overall accuracy of EAFP Med ST on chest X-ray, brain magnetic resonance imaging, and skin image datasets is 98.47%, 97.60%, and 99.06%, respectively, superior to all the compared state-of-the-art methods.

High dimensional and small samples characterize gene expression data and contain a large number of genes unrelated to disease. Feature selection improves the efficiency of disease diagnosis by selecting a small number of important genes. Unfortunately, existing algorithms do not consider the correlation between features, and search algorithms tend to fall into the local optimal solution in the feature search process. To this end, this paper proposes a feature filter and group evolution hybrid feature selection algorithm (FG-HFS) for high-dimensional gene expression data. Unlike existing algorithms, we propose using spectral clustering to group redundant features into a group. Then, we propose a redundant feature filter algorithm. According to the principle of approximate Markov blanket, grouped feature groups are filtered to delete these redundant features. Among them, filtered features are evenly divided by density according to the feature exponential strategy. Most importantly, we propose using the group evolution multi-objective genetic algorithm to search the filtered feature subsets and evaluate the candidate feature subsets according to the in-group and out-group so as to select the feature subsets with the highest accuracy and the least number. Experimental results show that the average accuracy (ACC) and Matthews correlation coefficient (MCC) indexes of the selected feature subsets (FSs) by the FG-HFS algorithm on 5 gene expression datasets are 92.76% and 88.76%, respectively, which are significantly better than the existing algorithms. In addition, the FSs and ACC/FSs indexes of the FG-HFS algorithm are also better than the existing algorithms, which fully proves the superiority of the FG-HFS algorithm. More importantly, the Wilcoxon and Friedman statistical experiments results show that the feature selection effect of FG-HFS algorithm is significantly better than that of existing algorithms, no matter in pairwise comparison or multiple comparison.

Integrating Internet of Things (IoT) technologies in the healthcare industry represents a transformative shift with tangible benefits. This paper provides a detailed examination of IoT adoption in healthcare, focusing on specific sensor types and communication methods. It underscores successful real-world applications, including remote patient monitoring, individualized treatment strategies, and streamlined healthcare delivery. Furthermore, it delves into the intricate challenges to realizing the full potential of IoT in healthcare. This includes addressing data security concerns, ensuring seamless interoperability, and optimizing the use of IoT-generated data. The paper seeks to inspire practitioners and researchers by highlighting the practical implications of IoT in healthcare, emphasizing the ways IoT can enhance patient care, resource allocation, and overall healthcare efficiency.

Brains are the control center of the nervous system in human bodies, and brain tumor is one of the most deadly diseases. Currently, magnetic resonance imaging (MRI) is the most effective way to brain tumors early detection in clinical diagnoses due to its superior imaging quality for soft tissues. Manual analysis of brain MRI is error-prone which depends on empirical experience and the fatigue state of the radiologists to a large extent. Computer-aided diagnosis (CAD) systems are becoming more and more impactful because they can provide accurate prediction results based on medical images with advanced techniques from computer vision. Therefore, a novel CAD method for brain tumor classification named RanMerFormer is presented in this paper. A pre-trained vision transformer is used as the backbone model. Then, a merging mechanism is proposed to remove the redundant tokens in the vision transformer, which improves computing efficiency substantially. Finally, a randomized vector functional-link serves as the head in the proposed RanMerFormer, which can be trained swiftly. All the simulation results are obtained from two public benchmark datasets, which reveal that the proposed RanMerFormer can achieve state-of-the-art performance for brain tumor classification. The trained RanMerFormer can be applied in real-world scenarios to assist in brain tumor diagnosis.

The Hybrid Parallel Fuzzy CNN (HP-FCNN) is a ground-breaking method for medical image analysis that combines the interpretive capacity of fuzzy logic with the capabilities of a convolutional neural network (CNN). This novel combination tackles problems related to brain image processing, reducing problems such as noise and hazy borders that are common in Magnetic Resonance Imaging (MRI). Unlike other CNN models, HP-FCNN combines fine-grained fuzzy representations with crisp CNN features, improving interpretability by displaying hidden layers. This insight into activation patterns facilitates comprehension of the decision-making processes necessary for the diagnosis of brain diseases. HP-FCNN outperforms other pretrained models (ResNet, DenseNet, VGG, and EfficientNet) on measures such as the confusion matrix and AUC-ROC, according to comparative assessments. Furthermore, the addition of Adaptive Class Activation Mapping (AD-CAM) enhances HPFCNN by identifying salient features during backpropagation and bolstering the network's capacity to enhance brain illness diagnosis and treatment planning. Our methodology, incorporating AD-CAM, yielded compelling results with a 96.86 F1-Score, 96.41 AUC, and 96.81 Accuracy, showcasing the effectiveness of our approach in achieving high-performance metrics in brain MRI analysis. With a 15% increase in accuracy, a 10% increase in sensitivity, and a 12% decrease in false positives, HP-FCNN outperforms its predecessors. These impressive advancements represent a quantifiable breakthrough in the capabilities of medical image processing technology; they are more than just anecdotal evidence.

Medical visual question answering (medical VQA) is a critical cross-modal interaction task that garnered considerable attention in the medical domain. Several existing methods commonly leverage the vision-and-language pretraining paradigms to mitigate the limitation of small-scale data. Nevertheless, most of them still suffer from two challenges that remain for further research: 1) limited research focuses on distilling representation from a complete modality to guide the representation learning of masked data in other modalities. 2) Multimodal fusion based on self-attention mechanisms cannot effectively handle the inherent uncertainty and vagueness of information interaction across modalities. To mitigate these issues, in this article, we propose a novel deep fuzzy multiteacher distillation (DFMD) network for medical VQA, which can take advantage of fuzzy logic to model the uncertainties from vison-language representations across modalities in a multiteacher framework. Specifically, a multiteacher knowledge distillation module is conceived to assist in reconstructing the missing semantics under the supervision signal generated by teachers from the other complete modality, achieving more robust semantic interaction across modalities. Incorporating insights from the fuzzy logic theory, we propose a noise-robust encoder called FuzBERT that enables our DFMD model to reduce the imprecision and ambiguity in feature representation during the multimodal interaction process. To the best of our knowledge, our work isthe first attemptto combine the fuzzy logic theory with the transformer-based encoder to effectively learn multimodal representation for medical VQA. Experimental results on the VQA-RAD and SLAKE datasets consistently demonstrate the superiority of our proposed DFMD method over state-of-the-art baselines.

Most of the previous machine learning-based models for multi-modal medical diagnosis, primarily designed for unimodal images, usually do not fully leverage the potential of multimodal medical images, leading to limited classification accuracy. These conventional methods typically focus only on the intermodality common information, neglecting the intra-modality specific information and assuming that the common information is more effective in disease diagnosis. Moreover, they do not adequately address the impact of fuzzy information between different medical imaging modalities on diagnostic results. To this end, we propose a Modality Fuzzy Information Separation Network for disease classification, which extracts both common and specific information from fuzzy information to construct a comprehensive representation of multi-modal medical images. Specifically, we extract modality invariant features as common information by explicitly modeling and maximizing loss constraints on mutual information. For specific information extraction, a constraint on feature space independence between specific and common information is imposed on each modality. Above two steps, we concatenate common information and specific information to construct a comprehensive multi-modal representation for separating fuzzy information. Finally, we purposely design a decoder network to reconstruct medical images from uni-modal specific information and common information to demonstrate the effectiveness of the modality fuzzy information separation network. We conducted a validation of the proposed method's performance in classifying cardiomegaly, pneumothorax, edema, and skin disease. The experimental results substantiate the effectiveness of our proposed approach.

Abstract Facial expression recognition (FER) is widely used in many fields. To further improve the accuracy of FER, this paper proposes a method based on double‐code LBP‐layer spatial‐attention network (DLSANet). The backbone model for the DLSANet is an emotion network (ENet), which is modified with a double‐code LBP (DLBP) layer and a spatial attention module. The DLBP layer is at the front of the first convolutional layer. More valuable features can be extracted by inputting the image processed by DLBP into convolutional layers. The JAFFE and CK+ datasets are used, which contain seven expressions: happiness, anger, disgust, neutral, fear, sadness, and surprise. The average of fivefold cross‐validation shows that DLSANet achieves a recognition accuracy of 93.81% and 98.68% on the JAFFE and CK+ datasets. The experiment reveals that the DLSANet can produce better classification results than state‐of‐the‐art methods.

Abstract Automatic brain tumour segmentation in MRI scans aims to separate the brain tumour's endoscopic core, edema, non‐enhancing tumour core, peritumoral edema, and enhancing tumour core from three‐dimensional MR voxels. Due to the wide range of brain tumour intensity, shape, location, and size, it is challenging to segment these regions automatically. UNet is the prime three‐dimensional CNN network performance source for medical imaging applications like brain tumour segmentation. This research proposes a context aware 3D ARDUNet (Attentional Residual Dropout UNet) network, a modified version of UNet to take advantage of the ResNet and soft attention. A novel residual dropout block (RDB) is implemented in the analytical encoder path to replace traditional UNet convolutional blocks to extract more contextual information. A unique Attentional Residual Dropout Block (ARDB) in the decoder path utilizes skip connections and attention gates to retrieve local and global contextual information. The attention gate enabled the Network to focus on the relevant part of the input image and suppress irrelevant details. Finally, the proposed Network assessed BRATS2018, BRATS2019, and BRATS2020 to some best‐in‐class segmentation approaches. The proposed Network achieved dice scores of 0.90, 0.92, and 0.93 for the whole tumour. On BRATS2018, BRATS2019, and BRATS2020, tumour core is 0.90, 0.92, 0.93, and enhancing tumour is 0.92, 0.93, 0.94.

Deep learning has become a primary choice in medical image analysis due to its powerful representation capability. However, most existing deep learning models designed for medical image classification can only perform well on a specific disease. The performance drops dramatically when it comes to other diseases. Generalizability remains a challenging problem. In this paper, we propose an evolutionary attention-based network (EDCA-Net), which is an effective and robust network for medical image classification tasks. To extract task-related features from a given medical dataset, we first propose the densely connected attentional network (DCA-Net) where feature maps are automatically channel-wise weighted, and the dense connectivity pattern is introduced to improve the efficiency of information flow. To improve the model capability and generalizability, we introduce two types of evolution: intra- and inter-evolution. The intra-evolution optimizes the weights of DCA-Net, while the inter-evolution allows two instances of DCA-Net to exchange training experience during training. The evolutionary DCA-Net is referred to as EDCA-Net. The EDCA-Net is evaluated on four publicly accessible medical datasets of different diseases. Experiments showed that the EDCA-Net outperforms the state-of-the-art methods on three datasets and achieves comparable performance on the last dataset, demonstrating good generalizability for medical image classification.

Oral diseases have a significant impact on human health, often going unnoticed in their early stages. Deep learning, a promising field in artificial intelligence, has shown remarkable success in various domains, especially dentistry. This paper aims to provide an overview of recent research on deep learning applications in dentistry, with a focus on dental imaging. Deep learning algorithms perform well in difficult tasks such as image segmentation and recognition, enabling accurate identification of oral conditions and abnormalities. Integration of deep learning with other oral health data offers a holistic understanding of the relationship between oral and systemic health. However, there are still many challenges that need to be addressed.

Internet of Medical Things (IoMT) refers to applying Internet of Things (IoT) into the medical field. The IoMT enables a medical system to connect various smart devices, such as wearable sensors, medical examination instruments, and hospital assets, for establishing an information platform. These smart devices act as the basic nodes in an IoMT system, collecting or generating health data and transmitting the data to the server for further processing and analysis. Physicians apply health data to make better medical decisions. Recently, the IoMT has been widely applied in many areas, including smart hospital, remote health monitoring, disease diagnosis, and infectious disease tracking. In this review, we investigated the IoMT from its concept and theory to its deployment domains, adopted technologies, and applications. We provided theoretical explanations with various examples and more than one hundred representative references. We also presented a cutting-edge discussion for the challenges and directions of the IoMT. We hoped that this systematic review would be beneficial to readers of all levels and backgrounds, including industry beginners, medical institution administrators, policy makers, and experienced researchers.

Inspired by the biological evolution, this paper proposes an evolutionary synthesis mechanism to automatically evolve DenseNet towards high sparsity and efficiency for medical image classification. Unlike traditional automatic design methods, this mechanism generates a sparser offspring in each generation based on its previous trained ancestor. Concretely, we use a synaptic model to mimic biological evolution in the asexual reproduction. Each generation’s knowledge is passed down to its descendant, and an environmental constraint limits the size of the descendant evolutionary DenseNet, moving the evolution process towards high sparsity. Additionally, to address the limitation of ensemble learning that requires multiple base networks to make decisions, we propose an evolution-based ensemble learning mechanism. It utilises the evolutionary synthesis scheme to generate highly sparse descendant networks, which can be used as base networks to perform ensemble learning in inference. This is specially useful in the extreme case when there is only a single network. Finally, we propose the MEEDNets (Medical Image Classification via Ensemble Bio-inspired Evolutionary DenseNets) model which consists of multiple evolutionary DenseNet-121s synthesised in the evolution process. Experimental results show that our bio-inspired evolutionary DenseNets are able to drop less important structures and compensate for the increasingly sparse architecture. In addition, our proposed MEEDNets model outperforms the state-of-the-art methods on two publicly accessible medical image datasets. All source code of this study is available at https://github.com/hengdezhu/MEEDNets.

In the global epidemic, distance learning occupies an increasingly important place in teaching and learning because of its great potential. This paper proposes a web-based app that includes a proposed 8-layered lightweight, customized convolutional neural network (LCCNN) for COVID-19 recognition. Five-channel data augmentation is proposed and used to help the model avoid overfitting. The LCCNN achieves an accuracy of 91.78%, which is higher than the other eight state-of-the-art methods. The results show that this web-based app provides a valuable diagnostic perspective on the patients and is an excellent way to facilitate medical education. Our LCCNN model is explainable for both radiologists and distance education users. Heat maps are generated where the lesions are clearly spotted. The LCCNN can detect from CT images the presence of lesions caused by COVID-19. This web-based app has a clear and simple interface, which is easy to use. With the help of this app, teachers can provide distance education and guide students clearly to understand the damage caused by COVID-19, which can increase interaction with students and stimulate their interest in learning.

Deep ensemble learning, where we combine knowledge learned from multiple individual neural networks, has been widely adopted to improve the performance of neural networks in deep learning. This field can be encompassed by committee learning, which includes the construction of neural network cascades. This study focuses on the high-dimensional low-sample-size (HDLS) domain and introduces multiple instance ensemble (MIE) as a novel stacking method for ensembles and cascades. In this study, our proposed approach reformulates the ensemble learning process as a multiple-instance learning problem. We utilise the multiple-instance learning solution of pooling operations to associate feature representations of base neural networks into joint representations as a method of stacking. This study explores various attention mechanisms and proposes two novel committee learning strategies with MIE. In addition, we utilise the capability of MIE to generate pseudo-base neural networks to provide a proof-of-concept for a "growing" neural network cascade that is unbounded by the number of base neural networks. We have shown that our approach provides (1) a class of alternative ensemble methods that performs comparably with various stacking ensemble methods and (2) a novel method for the generation of high-performing "growing" cascades. The approach has also been verified across multiple HDLS datasets, achieving high performance for binary classification tasks in the low-sample size regime.

Tuberculosis is a very deadly disease, with more than half of all tuberculosis cases dead in countries and regions with relatively poor health care resources. Fortunately, the disease is curable, and early diagnosis and medication can go a long way toward curing TB patients. Unfortunately, traditional methods of TB diagnosis rely on specialist doctors, which is lacking in areas with high TB mortality rates. Diagnostic methods based on artificial intelligence technology are one of the solutions to this problem. We propose a Deep Transferred EfficientNet with SVM (DTE-SVM), which replaces the pre-trained EfficientNet classification layer with an SVM classifier and achieves auspicious performance on a small dataset. After ten runs of 10-fold Cross-Validation, the DTE-SVM has a sensitivity of 93.89±1.96, a specificity of 95.35±1.31, a precision of 95.30±1.24, an accuracy of 94.62±1.00, and an F1-score of 94.62±1.00. In addition, our study conducted ablation studies on the effect of the SVM classifier on model performance and briefly discussed the results.

In Healthcare, the Internet of Things (IoT)-enabled surveillance cameras capture thousands of images every day, where face recognition provides reliable security as well as smart treatment through patient sentiment analysis, emotion detection, automated nurse calls, and hospital traffic systems. In this article, a secure face recognition system for the IoT-enabled Healthcare system has been proposed. Here each registered person will be identified by his/her face biometric with strong template protection schemes. To protect the biometric information, three-step template protection techniques are proposed: (i) Cancelable biometrics , (ii) BioCrypto-Circuit , and (iii) BioCrypto-Protection . The performance of the proposed system has been tested on four benchmark face databases, CVL, IITK, Casia-Face-v5, and FERET. The results of the proposed system are reported in terms of the correct recognition rate and the equal error rate. These performances have also been compared with some state-of-the-art methods with respect to each employed database, which shows the novelty of the proposed system.

Abstract Aim Fruit category classification is important in factory packing and transportation, price prediction, dietary intake, and so forth. Methods This study proposed a novel artificial intelligence system to classify fruit categories. First, 2D fractional Fourier entropy with rotation angle vector grid was used to extract features from fruit images. Afterwards, a five‐layer stacked sparse autoencoder was used as the classifier. Results Ten runs on the test set showed our method achieved a micro‐averaged F1 score of 95.08% for an 18‐category fruit dataset. Conclusion Our method gives better micro‐averaged F1 score than 10 state‐of‐the‐art approaches.

COVID-19 pneumonia started in December 2019 and caused large casualties and huge economic losses. In this study, we intended to develop a computer-aided diagnosis system based on artificial intelligence to automatically identify the COVID-19 in chest computed tomography images. We utilized transfer learning to obtain the image-level representation (ILR) based on the backbone deep convolutional neural network. Then, a novel neighboring aware representation (NAR) was proposed to exploit the neighboring relationships between the ILR vectors. To obtain the neighboring information in the feature space of the ILRs, an ILR graph was generated based on the k-nearest neighbors algorithm, in which the ILRs were linked with their k-nearest neighboring ILRs. Afterward, the NARs were computed by the fusion of the ILRs and the graph. On the basis of this representation, a novel end-to-end COVID-19 classification architecture called neighboring aware graph neural network (NAGNN) was proposed. The private and public data sets were used for evaluation in the experiments. Results revealed that our NAGNN outperformed all the 10 state-of-the-art methods in terms of generalization ability. Therefore, the proposed NAGNN is effective in detecting COVID-19, which can be used in clinical diagnosis.

In recent years, we witnessed a speeding development of deep learning in computer vision fields like categorization, detection, and semantic segmentation. Within several years after the emergence of AlexNet, the performance of deep neural networks has already surpassed human being experts in certain areas and showed great potential in applications such as medical image analysis. The development of automated breast cancer detection systems that integrate deep learning has received wide attention from the community. Breast cancer, a major killer of females that results in millions of deaths, can be controlled even be cured given that it is detected at an early stage with sophisticated systems. In this paper, we reviewed breast cancer diagnosis, detection, and segmentation computer-aided (CAD) systems based on state-of-the-art deep convolutional neural networks. The available data sets also indirectly determine CAD systems' performance, so we introduced and discussed the details of public data sets. The challenges remaining in CAD systems for breast cancer are discussed at the end of this paper. The highlights of this survey mainly come from three following aspects. First, we covered a wide range of the basics of breast cancer from imaging modalities to popular databases in the community; Second, we presented the key elements in deep learning to form the compactness for methods mentioned in reviewed papers; Third and lastly, the summative details in each reviewed paper are provided so that interested readers can have a refined version of these works without referring to original papers. Therefore, this systematic survey suits readers with varied backgrounds and will be beneficial to them.

Abstract Extreme learning machine (ELM) is a training algorithm for single hidden layer feedforward neural network (SLFN), which converges much faster than traditional methods and yields promising performance. In this paper, we hope to present a comprehensive review on ELM. Firstly, we will focus on the theoretical analysis including universal approximation theory and generalization. Then, the various improvements are listed, which help ELM works better in terms of stability, efficiency, and accuracy. Because of its outstanding performance, ELM has been successfully applied in many real-time learning tasks for classification, clustering, and regression. Besides, we report the applications of ELM in medical imaging: MRI, CT, and mammogram. The controversies of ELM were also discussed in this paper. We aim to report these advances and find some future perspectives.

The papers in this special section focus on the emerging challenges for deep learningin the biomedical industry. Due to the proliferation of biomedical imaging modalities such as Photoacoustic Tomography, Computed Tomography (CT), Optical Microscopy and Tomography, Single Photon Emission Computed Tomography (SPECT), Magnetic Resonance (MR) Imaging, Ultrasound, Positron Emission Tomography (PET), Magnetic Particle Imaging, EE/MEG, Electron Tomography, and Atomic Force Microscopy, massive amounts of biomedical and health informatics data are being generated on a daily basis. How can we utilize such big data to build better health profiles and predictive models so that we can better diagnose and treat diseases and provide a better life for humans? In the past years, many successful learning methods such as deep learning were proposed to answer this crucial question, which has social, economic, as well as legal implications.

Unsupervised domain adaptation for semantic segmentation aims to transfer knowledge from a labeled source domain to another unlabeled target domain. However, due to the label noise and domain mismatch, learning directly from source domain data tends to have poor performance. Though adversarial learning methods strive to reduce domain discrepancies by aligning feature distributions, traditional methods suffer from the training imbalance and feature distortion problems. Besides, due to the absence of target domain labels, the classifier is blind to features from the target domain during training. Consequently, the final classifier overfits the source domain features and usually fails to predict the structured outputs of the target domain. To alleviate these problems, we focus on enhancing the adversarial learning based feature alignment from three perspectives. First, a classification constrained discriminator is proposed to balance the adversarial training and alleviate the feature distortion problem. Next, to alleviate the classifier overfitting problem, self-training is collaboratively used to learn a domain robust classifier with target domain pseudo labels. Moreover, an efficient class centroid calculation module is proposed and the domain discrepancy is further reduced by aligning the feature centroids of the same class from different domains. Experimental evaluations on GTA5$\rightarrow$Cityscapes and SYNTHIA$\rightarrow$Cityscapes demonstrate state-of-the-art results compared to other counterpart methods. The source code and models have been made available at.11[Online]. Available:https://github.com/NUST-Machine-Intelligence-Laboratory/EFA.

In this article, we focus on the dynamic facial emotion recognition from online video. We combine deep neural networks with transfer learning theory and propose a novel model named DT-EFER. In detail, DT-EFER uses GoogLeNet to extract the deep features of key images from video clips. Then to solve the dynamic facial emotion recognition scenario, the framework introduces transfer learning theory. Thus, to improve the recognition performance, model DT-EFER focuses on the differences between key images instead of those images themselves. Moreover, the time complexity of this model is not high, even if previous exemplars are introduced here. In contrast to other exemplar-based models, experiments based on two datasets, namely, BAUM-1s and Extended Cohn–Kanade, have shown the efficiency of the proposed DT-EFER model.

Affective computing is one of the key technologies to achieve advanced brain-machine interfacing. It is increasingly concerning research orientation in the field of artificial intelligence. Emotion recognition is closely related to affective computing. Although emotion recognition based on electroencephalogram (EEG) has attracted more and more attention at home and abroad, subject-independent emotion recognition still faces enormous challenges. We proposed a subject-independent emotion recognition algorithm based on dynamic empirical convolutional neural network (DECNN) in view of the challenges. Combining the advantages of empirical mode decomposition (EMD) and differential entropy (DE), we proposed a dynamic differential entropy (DDE) algorithm to extract the features of EEG signals. After that, the extracted DDE features were classified by convolutional neural networks (CNN). Finally, the proposed algorithm is verified on SJTU Emotion EEG Dataset (SEED). In addition, we discuss the brain area closely related to emotion and design the best profile of electrode placements to reduce the calculation and complexity. Experimental results show that the accuracy of this algorithm is 3.53 percent higher than that of the state-of-the-art emotion recognition methods. What's more, we studied the key electrodes for EEG emotion recognition, which is of guiding significance for the development of wearable EEG devices.

Mixing patterns (MPs) in social trust networks (STNs) are increasingly attracting attention because they can assist analysts in designing information dissemination tactics and planning electronic word-of-mouth (eWOM) campaigns. However, the existing studies on MPs do not explain the assortative or disassortative tendencies of STNs due to their omission of the support of the sociological theory, as well as that of network theory. To address this issue, this study investigates the MPs in STNs from the standpoint of social identity theory (SIT). The user trust networks (UTNs) are modeled by a directed multigraph (DMG). Then, the structural properties of homogeneous trust networks and heterogeneous trust networks are explored via measures that include degree centrality, the correlation coefficient (CC), the cumulative distribution of the ratio of trust degree to distrust degree (CDRTD), and the assortativity coefficient. The MPs of homogeneous trust networks and heterogeneous trust networks are explained from the perspective of SIT. An experiential evaluation is conducted in the constructed homogeneous trust networks and heterogeneous trust networks using a real-world data set crawled from Epinions. The research findings indicate that the MPs in homogeneous trust networks tend toward assortative mixing (AM), and those in heterogeneous trust networks tend toward disassortative mixing (DM). The experimental results show that the performance of the proposed approach is superior to that of the state-of-the-art approach to influential user identification.

Traditional deep learning methods are sub-optimal in classifying ambiguity features, which often arise in noisy and hard to predict categories, especially, to distinguish semantic scoring. Semantic scoring, depending on semantic logic to implement evaluation, inevitably contains fuzzy description and misses some concepts, for example, the ambiguous relationship between normal and probably normal always presents unclear boundaries (normal - more likely normal - probably normal). Thus, human error is common when annotating images. Differing from existing methods that focus on modifying kernel structure of neural networks, this study proposes a dominant fuzzy fully connected layer (FFCL) for Breast Imaging Reporting and Data System (BI-RADS) scoring and validates the universality of this proposed structure. This proposed model aims to develop complementary properties of scoring for semantic paradigms, while constructing fuzzy rules based on analyzing human thought patterns, and to particularly reduce the influence of semantic conglutination. Specifically, this semantic-sensitive defuzzier layer projects features occupied by relative categories into semantic space, and a fuzzy decoder modifies probabilities of the last output layer referring to the global trend. Moreover, the ambiguous semantic space between two relative categories shrinks during the learning phases, as the positive and negative growth trends of one category appearing among its relatives were considered. We first used the Euclidean Distance (ED) to zoom in the distance between the real scores and the predicted scores, and then employed two sample t test method to evidence the advantage of the FFCL architecture. Extensive experimental results performed on the CBIS-DDSM dataset show that our FFCL structure can achieve superior performances for both triple and multiclass classification in BI-RADS scoring, outperforming the state-of-the-art methods.

Semantic segmentation and depth estimation play important roles in the field of autonomous driving. In recent years, the advantages of Convolutional Neural Networks (CNNs) have allowed these two topics to flourish. However, people always solve these two tasks separately and rarely solve them in a united model. In this paper, we propose a Mutual Encouragement Network (MENet), which includes a semantic segmentation branch and a disparity regression branch, and simultaneously generates semantic map and visual disparity. In the cost volume construction phase, the depth information is embedded in the semantic segmentation branch to increase contextual understanding. Similarly, the semantic information is also included in the disparity regression branch to generate more accurate disparity. Two branches mutually promote each other during training phase and inference phase. We conducted our method on the popular dataset KITTI, and the experimental results show that our method can outperform the state-of-the-art methods on both visual disparity generation and semantic segmentation. In addition, extensive ablation studies also demonstrate that the two tasks in our method can facilitate each other significantly with the proposed approach.

The new coronavirus COVID-19 has been spreading all over the world in the last six months, and the death toll is still rising. The accurate diagnosis of COVID-19 is an emergent task as to stop the spreading of the virus. In this paper, we proposed to leverage image feature fusion for the diagnosis of COVID-19 in lung window computed tomography (CT). Initially, ResNet-18 and ResNet-50 were selected as the backbone deep networks to generate corresponding image representations from the CT images. Second, the representative information extracted from the two networks was fused by discriminant correlation analysis to obtain refined image features. Third, three randomized neural networks (RNNs): extreme learning machine, Schmidt neural network and random vector functional-link net, were trained using the refined features, and the predictions of the three RNNs were ensembled to get a more robust classification performance. Experiment results based on five-fold cross validation suggested that our method outperformed state-of-the-art algorithms in the diagnosis of COVID-19.

As one of the important tools of epilepsy diagnosis, the electroencephalogram (EEG) is noninvasive and presents no traumatic injury to patients. It contains a lot of physiological and pathological information that is easy to obtain. The automatic classification of epileptic EEG is important in the diagnosis and therapeutic efficacy of epileptics. In this article, an explainable graph feature convolutional neural network named WTRPNet is proposed for epileptic EEG classification. Since WTRPNet is constructed by a recurrence plot in the wavelet domain, it can fully obtain the graph feature of the EEG signal, which is established by an explainable graph features extracted layer called WTRP block . The proposed method shows superior performance over state-of-the-art methods. Experimental results show that our algorithm has achieved an accuracy of 99.67% in classification of focal and nonfocal epileptic EEG, which proves the effectiveness of the classification and detection of epileptic EEG.

Aim: This study proposes a new artificial intelligence model based on cardiovascular computed tomography for more efficient and precise recognition of Tetralogy of Fallot (TOF). Methods: Our model is a structurally optimized stochastic pooling convolutional neural network (SOSPCNN), which combines stochastic pooling, structural optimization, and convolutional neural network. In addition, multiple-way data augmentation is used to overcome overfitting. Grad-CAM is employed to provide explainability to the proposed SOSPCNN model. Meanwhile, both desktop and web apps are developed based on this SOSPCNN model. Results: The results on ten runs of 10-fold cross-validation show that our SOSPCNN model yields a sensitivity of 92.25±2.19, a specificity of 92.75±2.49, a precision of 92.79±2.29, an accuracy of 92.50±1.18, an F1 score of 92.48±1.17, an MCC of 85.06±2.38, an FMI of 92.50±1.17, and an AUC of 0.9587. Conclusion: The SOSPCNN method performed better than three state-of-the-art TOF recognition approaches.

Myocardial infarction (MI), more commonly known as heart attack, occurs when the blood flow to the heart decreases or stops. Over 100,000 people each year in the UK suffer from an MI according to the report by British Heart Foundation. Following an MI, there is irreversible heart muscle damage that will become scar. The amount of scar following larger heart attacks, ST segment elevation myocardial infarction, drives enlargement of the heart and is associated with worse prognosis (increased risk of death and subsequent heart failure). Cardiac Magnetic Resonance Imaging (MRI) late gadolinium enhancement (LGE) has become the "gold standard" for the visualization of MI. However, to date, no "gold standard" fully automated methods exist for the quantification of MI from MRI.In this work, we propose an approach to construct such methods using Artificial Intelligence (AI) and Machine Learning (ML) technologies, in particular, Convolutional Neural Networks (CNN). Uncertainties, variability, and a possibility of bias inherent to any data imply that data-driven systems which are intended for use in clinical research and practice must be capable of learning from mistakes on-the-job. Here we develop and test a first deep learning CNN system with error correction capabilities (CNNEC) for the detection and quantification of MI. The system could be viewed as a proof-of-principle for the technology.

Crowd counting is of considerable significance to society in terms of public safety and urban development. Manual counting of people in a video or photo is often time-consuming and labour-intensive. People will need an efficient and economy way instead of counting manually. Nowadays, the convolutional neural network was popularly utilized as the baseline for crowd counting. However, the more complex the CNN-based algorithm, the more computing resources will be consumed. This article aims to present a simpler and faster fully optimized convolutional neural network for crowd counting with desired performance. To minimize the computational cost on training networks, we proposed a fully optimized method to build our network. Extensive experiments on our fully optimized convolutional neural network indicate the superiority of our network that has very high accuracy and speed on small scale crowd.

Summary Cerebral micro‐bleedings are small chronic brain hemorrhages caused by structural abnormalities of the small vessels. CMBs can be found from individuals with stroke at memory clinics and even healthy elderly people. CMBs indicate hemorrhage‐prone pathological states. Research shows that CMBs are associated with an increased risk of future ischemic stroke, intra‐cerebral hemorrhage (ICH), dementia, and death. Considering that CMBs severely influence people's life, it is necessary to identify the CMBs in an early stage to prevent from further deterioration and to help people live a healthy life. In this paper, we proposed using CNN with stochastic pooling for the CMB detection. CNN has good performance in image and video recognition, recommender system, and nature language processing. Based on the collected subject, the experiment result shows that the six‐convolution layer and three fully‐connected layer CNN, nine‐layers in total, achieved sensitivity, specificity, accuracy, and precision as 97.22%, and 97.35%, 97.28%, and 97.35% in average of ten runs, which shows better performance than five state‐of‐the‐art methods.

( Aim ) Multiple sclerosis is a neurological condition that may cause neurologic disability. Convolutional neural network can achieve good results, but tuning hyperparameters of CNN needs expert knowledge and are difficult and time-consuming. To identify multiple sclerosis more accurately, this article proposed a new transfer-learning-based approach. ( Method ) DenseNet-121, DenseNet-169, and DenseNet-201 neural networks were compared. In addition, we proposed the use of a composite learning factor (CLF) that assigns different learning factor to three types of layers: early frozen layers, middle layers, and late replaced layers. How to allocate layers into those three layers remains a problem. Hence, four transfer learning settings (viz., Settings A, B, C, and D) were tested and compared. A precomputation method was utilized to reduce the storage burden and accelerate the program. ( Results ) We observed that DenseNet-201-D (the layers from CP to T3 are frozen, the layers of D4 are updated with learning factor of 1, and the final new layers of FCL are randomly initialized with learning factor of 10) can achieve the best performance. The sensitivity, specificity, and accuracy of DenseNet-201-D was 98.27± 0.58, 98.35± 0.69, and 98.31± 0.53, respectively. ( Conclusion ) Our method gives better performances than state-of-the-art approaches. Furthermore, this composite learning rate gives superior results to traditional simple learning factor (SLF) strategy.

The existence and distribution pattern of cerebral microbleeds (CMBs) are associated with some underlying aetiologies caused by intra-cerebral hemorrhage (ICH). CMBs as a kind of subclinical sign can be recognized via magnetic resonance (MR) imaging technique in a few years before the onset of the disease. Hence, detecting CMBs accurately is important for treating and preventing related cerebral disease. In this study, we employed convolution neural network (CNN) for CMBs detection because of its powerful ability in image recognition. In view of too many efforts on optimizing the structure of CNN for achieving a better performance, we introduced center loss, which can greatly enhance the discriminative power of the deeply learned features, to CMBs detection for the first time. It is found that the performances of convolution neural network (CNN) trained under the joint supervision of softmax loss and center loss were significantly better than that under the supervision of softmax loss, even if there are few mislabelled samples in training data. With this trick, we achieved a high performance with a sensitivity of 98.869 ± 1.026%, a specificity of 96.491 ± 0.367%, and an accuracy of 97.681 ± 0.497%, which is better than four state-of-the-art methods.

Breast cancer is one of the common cancers threatening the health of women while the incident rate of it is quite low in men to contribute to a major killer of men. Early syndromes of breast cancer including micro-calcification, mass, and distortion in mammography images can be very helpful for rad iologists to make diagnosis of the cancer at early stage, which means the cancer can be treated or even be cured timely and thus make early diagnosis important. To assist radiologists with diagnosis, we set up a computer-aided diagnosis system to make diagnosis decision of breast cancer in this paper. We acquired regions of interests in mammographic images from public database, and labeled regions containing micro-calcification or mass as abnormality while regions without such abnormalities as normality. By transferring the state-of-the-art networks into our quest, we found that ResNet18 performed best and achieved mean accuracy of 95.91%.

The α-order fractional derivative of the Dirichlet η function is computed in order to investigate the behavior of the fractional derivative of the Riemann zeta function ζ(α) on the critical strip. The convergence of η(α) is studied. In particular, its half-plane of convergence gives the possibility to better understand the ζ(α) and its critical strip. As an application, two signal processing networks, corresponding to η(α) and to its Fourier transform respectively, are shortly described.

(Aim) In order to detect pathological brains in a more efficient way, (Method) we proposed a novel system of pathological brain detection (PBD) that combined wavelet packet Tsallis entropy (WPTE), feedforward neural network (FNN), and real-coded biogeography-based optimization (RCBBO). (Results) Th e experiments showed the proposed WPTE + FNN + RCBBO approach yielded an average accuracy of 99.49% over a 255-image dataset. (Conclusions) The WPTE + FNN + RCBBO performed better than 10 state-of-the-art approaches.

(Objective) In order to increase classification accuracy of tea-category identification (TCI) system, this paper proposed a novel approach. (Method) The proposed methods first extracted 64 color histogram to obtain color information, and 16 wavelet packet entropy to obtain the texture information. With the aim of reducing the 80 features, principal component analysis was harnessed. The reduced features were used as input to generalized eigenvalue proximal support vector machine (GEPSVM). Winner-takes-all (WTA) was used to handle the multiclass problem. Two kernels were tested, linear kernel and Radial basis function (RBF) kernel. Ten repetitions of 10-fold stratified cross validation technique were used to estimate the out-of-sample errors. We named our method as GEPSVM + RBF + WTA and GEPSVM + WTA. (Result) The results showed that PCA reduced the 80 features to merely five with explaining 99.90% of total variance. The recall rate of GEPSVM + RBF + WTA achieved the highest overall recall rate of 97.9%. (Conclusion) This was higher than the result of GEPSVM + WTA and other five state-of-the-art algorithms: back propagation neural network, RBF support vector machine, genetic neural-network, linear discriminant analysis, and fitness-scaling chaotic artificial bee colony artificial neural network.

(Aim) Abnormal breast can be diagnosed using the digital mammography. Traditional manual interpretation method cannot yield high accuracy. (Method) In this study, we proposed a novel computer-aided diagnosis system for detecting abnormal breasts in mammogram images. First, we segmented the region-o f-interest. Next, the weighted-type fractional Fourier transform (WFRFT) was employed to obtain the unified time-frequency spectrum. Third, principal component analysis (PCA) was introduced and used to reduce the spectrum to only 18 principal components. Fourth, feed-forward neural network (FNN) was utilized to generate the classifier. Finally, a novel algorithm-specific parameter free approach, Jaya, was employed to train the classifier. (Results) Our proposed WFRFT + PCA + Jaya-FNN achieved sensitivity of 92.26% ± 3.44%, specificity of 92.28% ± 3.58%, and accuracy of 92.27% ± 3.49%. (Conclusions) The proposed CAD system is effective in detecting abnormal breasts and performs better than 5 state-of-the-art systems. Besides, Jaya is more effective in training FNN than BP, MBP, GA, SA, and PSO.

To detect the sensorineural hearing loss (SNHL) from healthy people accurately, we used magnetic resonance imaging (MRI) to obtain the imaging data, and then proposed a new computer-aided diagnosis (CAD) system, on the basis of texture analysis method. In the first, we extracted 12-element feature from each brain image via fractional Fourier entropy (FRFE). Afterwards, multilayer perceptron (MLP) was employed as the classifier, which was trained by a novel fitness-scaling adaptive genetic algorithm (FSAGA). The statistical analysis over 49 subjects showed the overall accuracy of our method yielded 95.51%. Experimental results performed better than four state-of-the-art weight optimization methods, and this CAD system give significantly better performance than manual interpretation.

The Fractional Fourier transform (FRFT) is a relatively novel linear transforms that is a generalization of conventional Fourier transform (FT). FRFT can transform a particular signal to a unified time-frequency domain. In this survey, we try to present a comprehensive investigation of FRFT. Firstl y, we provided definition of FRFT and its three discrete versions (weighted-type, sampling-type, and eigendecomposition-type). Secondly, we offered a comprehensive theoretical research and technological studies that consisted of hardware implementation, software implementation, and optimal order selection. Thirdly, we presented a survey on applications of FRFT to following fields: communication, encryption, optimal engineering, radiology, remote sensing, fractional calculus, fractional wavelet transform, pseudo-differential operator, pattern recognition, and image processing. It is hoped that this survey would be beneficial for the researchers studying on FRFT.

Background: We proposed a new automatic and rapid computer-aided diagnosis system to detect pathological brain images obtained in the scans of magnetic resonance imaging (MRI). Methods: For simplification, we transformed the problem to a binary classification task (pathological or normal). It consisted of two steps: first, Hu moment invariants (HMI) were extracted from a specific MR brain image; then, seven HMI features were fed into two classifiers: twin support vector machine (TSVM) and generalised eigenvalue proximal SVM (GEPSVM). Results: Then, a 5 × 5-fold cross validation on a data set containing 90 MR brain images, demonstrated that the proposed methods “HMI + GEPSVM” and “HMI + TSVM” achieved classification accuracy of 98.89%, higher than eight state-of-the-art methods: “DWT + PCA + BP-NN”, “DWT + PCA + RBF-NN”, “DWT + PCA + PSO-KSVM”, “WE + BP-NN”, “WE + KSVM”, “DWT + PCA + GA-KSVM”, “WE + PSO-KSVM” and “WE + BBO-KSVM”. Conclusion: The proposed methods are superior to other methods on pathological brain detection (p < 0.05).

In order to detect cerebral microbleed more efficiently, we developed a novel computer-aided detection method based on susceptibility-weighted imaging. We enrolled five CADASIL patients and five healthy controls. We used a 20x20 neighboring window to generate samples on each slice of the volumetric brain images. The sparse autoencoder (SAE) was used to unsupervised feature learning. Then, a deep neural network was established using the learned features. The results over 10x10-fold cross validation showed our method yielded a sensitivity of 93.20±1.37%, a specificity of 93.25±1.38%, and an accuracy of 93.22±1.37%. Our result is better than Roy's method, which was proposed in 2015.

Abstract Accurate fruit classification is difficult to accomplish because of the similarities among the various categories. In this paper, we proposed a novel fruit‐classification system, with the goal of recognizing fruits in a more efficient way. Our methodology included the following steps. First, a four‐step pre‐processing was employed. Second, the features (colour, shape, and texture) were extracted. Third, we utilized principal component analysis to remove excessive features. Fourth, a novel fruit‐classification system based on biogeography‐based optimization (BBO) and feedforward neural network (FNN) was proposed, with the short name of BBO‐FNN. The experiment employed over 1653 chromatic fruit images (18 categories) by fivefold stratified cross‐validation. The results showed that the proposed BBO‐FNN yielded an overall accuracy of 89.11%, which was higher than the five state‐of‐the‐art methods: genetic algorithm‐FNN, artificial bee colony‐FNN, particle swarm optimization‐FNN, kernel support vector machine, and ant colony optimization‐FNN. Also, the BBO‐FNN achieved the same accuracy as fitness‐scaling chaotic artificial bee colony‐FNN, but it performed much faster than the latter. The proposed BBO‐FNN was effective in fruit‐classification in terms of classification accuracy and computation time. This indicated that it can be applied in credible use.