Despite advances in deep learning (DL) and computer vision, breast cancer (BC) detection via ultrasound remains challenging. Existing methods often focus on single tasks using complex pipelines and publicly available datasets, limiting clinical applicability. To address this, we propose BreastUS-Net-a novel architecture for hierarchical BC classification using diverse datasets. Our approach uses a dual-branch MobileNet architecture with fine-tuned and frozen layers to capture both task-specific and general features, eliminating manual feature extraction. These features are then fused to create a comprehensive representation, which is subsequently aggregated and refined. The aggregation step merges the outputs from both branches, while the refinement module reduces complexity, highlights relevant patterns, and mitigates overfitting to improve generalization. Additionally, we integrate a multihead self-attention (MHSA) block to highlight diagnostically significant regions in ultrasound images, enhancing both accuracy and robustness. Finally, the orthogonal softmax layer (OSL) boosts discriminative power by enforcing orthogonality among weight vectors, reducing parameter co-adaptation and enabling more effective optimization. We used six diverse datasets from multiple centers, including: a large Zhejiang Cancer Hospital set (2,171 images), public BUSI dataset (780 images), external test sets from Yunnan Cancer Hospital (351 images) and Sir Run Run Shaw Hospitals (365 images), fibroadenoma (FA) vs. phyllodes tumor (PT) classification, and a PT grading dataset. We use explainable AI (XAI) techniques-Grad-CAM, SHAP, and saliency maps-to enhance trust in breast ultrasound predictions. Our model achieves state-of-the-art performance, with accuracies of 94.48% on a clinical dataset and 94.23% on the BUSI dataset, highlighting its potential to improve BC diagnosis and personalized treatment.

Accurate detection of gastrointestinal (GI) diseases is crucial due to their high prevalence. Screening is often inefficient with existing methods, and the complexity of medical images challenges single‐model approaches. Leveraging diverse model features can improve accuracy and simplify detection. In this study, we introduce a novel deep learning model tailored for the diagnosis of GI diseases through the analysis of endoscopy images. This innovative model, named MultiResFF‐Net, employs a multilevel residual block‐based feature fusion network. The key strategy involves the integration of features from truncated DenseNet121 and MobileNet architectures. This fusion not only optimizes the model’s diagnostic performance but also strategically minimizes complexity and computational demands, making MultiResFF‐Net a valuable tool for efficient and accurate disease diagnosis in GI endoscopy images. A pivotal component enhancing the model’s performance is the introduction of the Modified MultiRes‐Block (MMRes‐Block) and the Convolutional Block Attention Module (CBAM). The MMRes‐Block, a customized residual learning component, optimally handles fused features at the endpoint of both models, fostering richer feature sets without escalating parameters. Simultaneously, the CBAM ensures dynamic recalibration of feature maps, emphasizing relevant channels and spatial locations. This dual incorporation significantly reduces overfitting, augments precision, and refines the feature extraction process. Extensive evaluations on three diverse datasets—endoscopic images, GastroVision data, and histopathological images—demonstrate exceptional accuracy of 99.37%, 97.47%, and 99.80%, respectively. Notably, MultiResFF‐Net achieves superior efficiency, requiring only 2.22 MFLOPS and 0.47 million parameters, outperforming state‐of‐the‐art models in both accuracy and cost‐effectiveness. These results establish MultiResFF‐Net as a robust and practical diagnostic tool for GI disease detection.

Lung cancer ranks among the top causes of cancer mortality worldwide. However, early detection can significantly boost survival rates, potentially increasing them by as much as 70%. While deep learning algorithms have shown promise in detecting lung cancer from CT scans, current methods are often inefficient, and single-model approaches struggle with complex medical images. Leveraging diverse model features can enhance accuracy and simplify detection. This paper introduces LungX-Net, an innovative and cost-effective deep learning model aimed at improving lung cancer diagnostic accuracy using CT and histopathological images. The proposed model unifies two networks into a unified, trainable feature extraction architecture, employing model compression to preserve essential core blocks. This approach enhances the model’s ability to propagate strong and robust features while significantly reducing computational resource requirements. The model also integrates a Convolutional Block Attention Module (CBAM) to enhance feature extraction by emphasizing the most important regions, which further improves overall accuracy. The integration of these techniques optimizes diagnostic performance while strategically minimizing complexity, making LungX-Net a powerful tool for efficient and precise lung cancer diagnosis. The proposed LungX-Net was evaluated on both CT and histopathological datasets, achieving a recognition accuracy of 99.09% on the CT dataset and 99.30% on the histopathological dataset. LungX-Net demonstrated superior performance compared to previous approaches, achieving greater accuracy with fewer parameters and faster testing times, highlighting its efficiency and cost-effectiveness.

COVID-19 has rapidly spread across the world as an extremely contagious disease. Therefore, early detection of the virus is essential to effectively control its transmission and prevent further spread. It is widely reported in the literature that convolutional neural networks (CNNs) are commonly utilized for COVID-19 detection. However, the majority of these models require substantial computational resources and possess a large number of parameters, rendering them less feasible for deployment on real-time devices. This study proposes a solution to address COVID-19 detection challenges by introducing LWIA-Net, a rapid and highly efficient lightweight CNN architecture. The LWIA-Net is equipped with channel attention squeeze and excitation (SE) blocks, as well as a naïve inception module, to enhance the network’s learning ability, reduce computational complexity, and maintain a high level of detection performance. By incorporating the naïve inception module, the LWIA-Net enables multi-level feature extraction, while the inclusion of the SE block helps focuses on informative channels. This integration contributes to the production of high-quality image features and effectively reduces redundancy. We performed thorough experiments using both our locally developed dataset and a publicly available dataset. The statistical analysis demonstrate that LWIA-Net achieves superior performance compared to pre-trained CNN architectures, despite its extremely lightweight architecture which demands low computational cost, memory space, and consists of only 1.11 million parameters. We conducted a comprehensive ablation study to gain a deeper understanding of the significance of each component and to verify their individual contributions to the LWIA-Net. Additionally, we utilized Grad-CAM analysis to confirm that the proposed model effectively identifies and emphasizes the most critical regions of interest. Experimental results have shown that LWIA-Net achieves remarkable accuracy rates of 94.45% and 97.56% on a local dataset and a public dataset. These findings suggest that LWIA-Net could potentially assist medical professionals in identifying patients with COVID-19 infection.

In the domain of medical diagnostics, precise identification of various skin and oral diseases is vital for effective patient care. In particular, Mpox is a potentially dangerous viral disease with zoonotic origins, capable of human-to-human transmission, underscoring the urgency of precise diagnostic methods for timely intervention. This paper introduces a novel approach named the Choquet Fuzzy Integral-based Ensemble (CFI-Net) for accurate classification of skin diseases, with a specific emphasis on detecting Mpox, foot ulcers, and various mouth and oral diseases. Our methodology begins with Transfer Learning, enhancing the classification capabilities of base classifiers (DenseNet169, MobileNetV1 and DenseNet201) by incorporating additional layers. Subsequently, we aggregate the prediction scores from each base classifier using the Choquet fuzzy integral (CFI) to derive the final predicted labels, thus ensuring dynamic and robust predictions. Fuzzy measures, a crucial component of this fuzzy integral-based ensemble method, are typically determined through manual experimentation in previous approaches. However, in our study, we have tackled the challenge of manual tuning by employing meta-heuristic optimization algorithm to precisely configure the fuzzy measures for optimal performance. A rigorous evaluation is conducted on four publicly available datasets, encompassing two Mpox datasets, a foot ulcer dataset, and a mouth and oral disease dataset. The experiments reveal the remarkable effectiveness of CFI-Net in significantly improving disease classification accuracy. Additionally, we employ Grad-CAM analysis to provide insights into the decision-making processes of our models. Our findings underscore the exceptional performance of CFI-Net, achieving accuracy rates of 98.06% and 94.81% for Mpox detection, 99.06% for foot ulcer detection, and an impressive 99.61% for mouth and oral disease classification. This research not only contributes to the advancement of disease diagnosis but also demonstrates the effectiveness of ensemble learning techniques coupled with fuzzy integral-based fusion in enhancing diagnostic accuracy.

Abstract Coronavirus disease (COVID‐19) is a pandemic that has caused thousands of casualties and impacts all over the world. Most countries are facing a shortage of COVID‐19 test kits in hospitals due to the daily increase in the number of cases. Early detection of COVID‐19 can protect people from severe infection. Unfortunately, COVID‐19 can be misdiagnosed as pneumonia or other illness and can lead to patient death. Therefore, in order to avoid the spread of COVID‐19 among the population, it is necessary to implement an automated early diagnostic system as a rapid alternative diagnostic system. Several researchers have done very well in detecting COVID‐19; however, most of them have lower accuracy and overfitting issues that make early screening of COVID‐19 difficult. Transfer learning is the most successful technique to solve this problem with higher accuracy. In this paper, we studied the feasibility of applying transfer learning and added our own classifier to automatically classify COVID‐19 because transfer learning is very suitable for medical imaging due to the limited availability of data. In this work, we proposed a CNN model based on deep transfer learning technique using six different pre‐trained architectures, including VGG16, DenseNet201, MobileNetV2, ResNet50, Xception, and EfficientNetB0. A total of 3886 chest X‐rays (1200 cases of COVID‐19, 1341 healthy and 1345 cases of viral pneumonia) were used to study the effectiveness of the proposed CNN model. A comparative analysis of the proposed CNN models using three classes of chest X‐ray datasets was carried out in order to find the most suitable model. Experimental results show that the proposed CNN model based on VGG16 was able to accurately diagnose COVID‐19 patients with 97.84% accuracy, 97.90% precision, 97.89% sensitivity, and 97.89% of F1‐score. Evaluation of the test data shows that the proposed model produces the highest accuracy among CNNs and seems to be the most suitable choice for COVID‐19 classification. We believe that in this pandemic situation, this model will support healthcare professionals in improving patient screening.

Kidney abnormalities are common in the world and due to the scarcity of urologists, there is a need to develop an automated system for the detection of kidney diseases. In this paper, we present a powerful and unique deep transfer learning (TL) architecture based on a pre-trained VGG19 model and a naïve Inception module to efficiently detect major kidney diseases from CT images. We customized the architecture of the VGG19 model by removing the fully connected layers and placing a randomly initialized naïve Inception module and other layers such as batch normalization, dropout and dense layers to avoid vanishing gradient and overfitting problems and to improve performance. We consider two ways of TL: feature extractor and fine-tuning, which is technically implemented and evaluated. Grad-CAM and feature map techniques are used to visualize regions of interest that are meaningful for the diagnosis of kidney disease. 4000 kidney CT images were used to evaluate the performance of the proposed model using different performance metrics. Experimental results show that the proposed model exhibits superior performance after applying fine-tuning compared to other method. The results showed that the proposed model with a fine-tuning strategy produced an excellent accuracy of 99.25% in detecting three different kidney diseases from CT images.

COVID-19 is the most recent coronavirus-related disease that has been declared a pandemic by the World Health Organization (WHO), causing a global emergency that has resulted in a large number of deaths and is rapidly spreading around the world. It causes respiratory illness and is highly contagious, putting a strain on health and medical systems worldwide. With the help of various deep learning (DL) techniques, chest CT scans are considered an effective tool for diagnosing COVID-19 because it directly affects the lungs. In addition, the visual similarities between COVID-19 and pneumonia make identification even more challenging, as COVID-19 is also a virus. In this paper, we designed a unique lightweight DL model named CVD19-Net with fewer layers as an accurate diagnostic method for COVID-19. Different regularization techniques such as dropout layers, batch normalization layers and data augmentation are injected into the CVD19-Net model to improve classification accuracy and reduce overfitting. We considered three different publicly available datasets for our experiments. (1) Dataset 1: 2482 CT images were collected; (2) Dataset 2: 7544 CT images were collected; (3) Dataset 3: 3190 CT images were collected. The experimental results show that the proposed model achieves 98.59% accuracy on dataset 1, 98.21% on dataset 2, and 95.61% on dataset 3, which is better than the existing methods. The proposed model requires less training time and storage space, which makes it computationally efficient while maintaining a high level of accuracy, which can help clinicians quickly identify COVID-19 patients.

In the face of intensive computing tasks with massive data, cloud computing is difficult to provide high-quality services. Edge computing extends cloud services to the edge of the network by introducing edge devices between terminal devices and the cloud. For limited edge server resources, it is especially important to optimize offload strategies by accurately predicting the load on the terminal device. This paper proposes a C-LSTM prediction model based on deep neural network to predict the CPU utilization of terminal equipment in the future, and then proposes a distributed greedy algorithm for offloading decision. The simulation results show that the accuracy of C-LSTM prediction model is higher than other baseline models, reduces energy consumption and delay, and provides high-quality computing services.