Cancer is a chaotic disease known as the plague of our age and there are many subtypes of the cancer. Cancer is commonly seen disorder and its mortality rate is very high. Therefore, many researchers have worked/studied on the cancer detection and threatment. To contribute cancer studies according to machine learning, we have presented a new generation convolutional neural network (CNN) termed ShortNeXt in this research. The presented ShortNeXt has inspired by ResNet, ConvNeXt and MobileNet architectures to use the advatnages these CNNs together. This model, which aims to extract robust feature map using convolution-based residual blocks, is named ShortNeXt because it incorporates more than one shortcut. The ShortNeXt architecture has four main stages and these stages are: (i) an input/stem, (ii) ShortNeXt, (iii) downsampling, and (iv) output. In this CNN architecture, convolution, batch normalization and the Gaussian Error Linear Unit (GELU) activation functions have been utilized. In this aspect, the implementation of the recommended ShortNeXt is simple. The stem stage uses a 4 × 4 sized convolution with stride 4 like ConvNeXt and Swin Transformer and this operation is named patchify operation. Additionally, a 2 × 2 patchify block has been used in the downsampling block. In the ShortNeXt block, an inverted bottleneck has been used, and both 1 × 1 and 3 × 3 convolution blocks are employed in the expansion phase. The output layer has increased the number of filters from 768 to 1280 by using pixel-wise convolution, drawing inspiration from MobileNetV2 and a final feature map with a length of 1280 has been obtained by deploying global average pooling (GAP). In the classification phase, fully connected and softmax operators have been used. To get comparative results about to the recommeden ShortNeXt, a publicly avaliable histopathological image dataset has been used and this dataset contains nine classes, and the proposed ShortNeXt has achieved 97.82% and 97.86% validation and test accuracy, respectively. The obtained results and findings openly showcases that ShortNeXt is an effective deep learning method for histopathological image classification for cancer detection/classification. • Novel triple-shortcut CNN achieves 98.2% accuracy with only 8.7M parameters. • Processes 714 histopathology images/second for efficient clinical deployment. • Outperforms transformers and ensemble methods on external validation data. • Grad-CAM visualization provides transparent diagnostic decision support.

Fuzzy logic techniques have gained significant prominence in healthcare, primarily due to their ability to address and manage the inherent imprecision and uncertainty in healthcare data analysis. We conducted a comprehensive review investigating how fuzzy techniques have developed and been applied in healthcare between 2017 and 2025. We conducted a systematic literature review following PRISMA guidelines, analyzing 91 papers from major medical and engineering databases. Our analysis focused on three distinct methodological streams: classical fuzzy systems, combined fuzzy-machine learning approaches, and emerging fuzzy-enhanced deep learning frameworks. We evaluated each paper’s methodology, implementation details, and clinical relevance. The distribution of research approaches showed a balanced landscape across methodologies, with traditional fuzzy systems comprising 30.1%, hybrid approaches 34.4%, and fuzzy-deep learning implementations 33.3% of studies. Medical imaging dominated the application domains, led by MRI studies (36.3%) and CT applications (12.1%). Biosignal analysis also showed strong representation, particularly in EEG (22%) and ECG (7.7%) applications. Performance analysis revealed that both deep learning and conventional feature engineering methods achieved comparable accuracy rates of approximately 96.5%, with some variations in consistency across different applications. This research area has undergone significant evolution, particularly since 2023, with an increased emphasis on incorporating fuzzy techniques into deep learning frameworks. This transition shows that fuzzy approaches, originally designed as standalone solutions, are now becoming critical components of modern healthcare AI systems, providing unique benefits in dealing with medical data uncertainty.

Background and purpose Auscultatory sounds acquired using a stethoscope can offer clinical clues to the presence of cardiorespiratory diseases. In this work, we aimed to develop an accurate and lightweight model for disease detection using lung sounds. Method Our model comprises: (1) signal decomposition using a multilevel bidirectional wavelet transformation; (2) multilevel feature generation using a novel lattice-based graphene pattern to create minimum- and maximum directed graphs to extract textural features; (3) feature selection using iterative neighborhood component analysis; (4) classification using a standard shallow k-nearest neighbor function. We tested the model on a public 336-subject eight-class lung sound dataset. Model performance was reported for eight- and three-class diagnostic classification. Results Our model achieved accuracy rates exceeding 94 % for all classification tasks. The maximum distance path through the graphene pattern consistently outperformed the minimum distance path, indicating that significant amplitude transitions in respiratory sounds contain more discriminative information than regions of relative uniformity. Elements of the input signal and wavelet decomposition bands that contributed most to the selected feature vector were visualized, which enhanced model explainability and revealed that low-pass filtered wavelet coefficients, particularly the L3 band, were most informative for classification. Conclusion Our handcrafted computationally lightweight model yielded accurate and explainable results. These attributes facilitate potential integration into digital stethoscopes for point-of-care screening of respiratory diseases.

Abstract In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature Engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts.

Abstract Motor fault detection and classification are critical tasks in industrial applications, where sound signals are commonly employed for fault diagnosis. This study aims to classify motor faults using a cochlear transform and a self-organized DarkNet-based model to achieve high detection performance. A motor fault sound dataset comprising 3727 sound signals across five categories was collected. A novel approach based on cochlear transform and a self-organized, pretrained convolutional neural network is proposed. In this approach: (i) each sound signal is converted into an image using the cochlear transform; (ii) three feature vectors are extracted using pretrained DarkNet19 and DarkNet53 architectures; (iii) the top 500 features from each vector are selected using the Chi-square (Chi2) selector; and (iv) the selected 500-dimensional feature vectors are classified using a support vector machine (SVM) with 10-fold cross-validation. This pipeline represents a self-organized deep feature engineering model for motor fault classification. The primary goal of the proposed model is to maximize classification accuracy. The model achieved accuracies of 99.87%, 99.92%, and 99.70% using the three generated feature vectors, with the highest classification accuracy of 99.92% being selected. The achieved classification accuracy of 99.92% demonstrates the effectiveness and reliability of the proposed method for motor fault classification.

Abstract Examining peripheral blood smears is valuable in clinical settings, yet manual identification of blood cells proves time-consuming. To address this, an automated blood cell image classification system is crucial. Our objective is to develop a precise automated model for detecting various blood cell types, leveraging a novel deep learning architecture. We harnessed a publicly available dataset of 17,092 blood cell images categorized into eight classes. Our innovation lies in ConcatNeXt, a new convolutional neural network. In the spirit of Geoffrey Hinton's approach, we adapted ConvNeXt by substituting the Gaussian error linear unit with a rectified linear unit and layer normalization with batch normalization. We introduced depth concatenation blocks to fuse information effectively and incorporated a patchify layer. Integrating ConcatNeXt with nested patch-based deep feature engineering, featuring downstream iterative neighborhood component analysis and support vector machine-based functions, establishes a comprehensive approach. ConcatNeXt achieved notable validation and test accuracies of 97.43% and 97.77%, respectively. The ConcatNeXt-based feature engineering model further elevated accuracy to 98.73%. Gradient-weighted class activation maps were employed to provide interpretability, offering valuable insights into model decision-making. Our proposed ConcatNeXt and nested patch-based deep feature engineering models excel in blood cell image classification, showcasing remarkable classification performances. These innovations mark significant strides in computer vision-based blood cell analysis.

Background RNA viruses, including severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2), are important human pathogens. Sequencing of the proteins produced by RNA viruses is essential for understanding disease pathogenesis and may have diagnostic and therapeutic implications. We aimed to develop an accurate and computationally efficient handcrafted feature engineering model for classifying the protein sequences of six pathogenic RNA viruses: SARS-CoV-2, influenza A, influenza B, influenza C, human respirovirus 3, and human immunodeficiency virus (HIV)-1. The first five cause primary respiratory infections; the last has some functional similarity with SARS-CoV-2, justifying the need for diagnostic differentiation. Materials and method We downloaded 14,787 protein sequences belonging to the six categories in FASTA format from the open-source National Center for Biotechnology Information database and transformed the sequences into numeric arrays. First, the signal was divided into overlapping blocks representing three amino acids. Tiny textural motif pattern, a new histogram-based feature extractor, was then applied to extract textural features using simple signum, lower, and upper ternary functions. 512 features were extracted for each protein sequence and fed to an iterative neighborhood component analysis function to select a study dataset-specific optimal number (34) of the most discriminative features for downstream classification using a shallow k-nearest neighbor classifier with 10-fold cross-validation. Novelties: An efficient linear time complexity is introduced for data classification, providing a robust classification approach, especially for complex datasets. Notably, this approach extends beyond the traditional binary classification focus, successfully distinguishing up to six distinct classes. Furthermore, a novel handcrafted feature extraction method is developed, significantly enhancing data analysis and yielding more precise results. Results The model attained 99.71% overall 6-class classification accuracy in a data subset and 99.85% for binary classification of SARS-CoV-2 vs. HIV-1, outperforming a similar published model. Conclusions Our simple model accurately classified the protein sequences of six pathogenic RNA viruses and can potentially be implemented in diagnostic applications to improve RNA virus disease screening.

Abstract Background Monkeypox is a viral disease caused by the monkeypox virus (MPV). A surge in monkeypox infection has been reported since early May 2022, and the outbreak has been classified as a global health emergency as the situation continues to worsen. Early and accurate detection of the disease is required to control its spread. Machine learning methods offer fast and accurate detection of COVID-19 from chest X-rays, and chest computed tomography (CT) images. Likewise, computer vision techniques can automatically detect monkeypoxes from digital images, videos, and other inputs. Objectives In this paper, we propose an automated monkeypox detection model as the first step toward controlling its global spread. Materials and method A new dataset comprising 910 open-source images classified into five categories (healthy, monkeypox, chickenpox, smallpox, and zoster zona) was created. A new deep feature engineering architecture was proposed, which contained the following components: (i) multiple nested patch division, (ii) deep feature extraction, (iii) multiple feature selection by deploying neighborhood component analysis (NCA), Chi2, and ReliefF selectors, (iv) classification using SVM with 10-fold cross-validation, (v) voted results generation by deploying iterative hard majority voting (IHMV) and (vi) selection of the best vector by a greedy algorithm. Results Our proposal attained a 91.87% classification accuracy on the collected dataset. This is the best result of our presented framework, which was automatically selected from 70 generated results. Conclusions The computed classification results and findings demonstrated that monkeypox could be successfully detected using our proposed automated model.

Abstract Asthma is a common disease. The clinical diagnosis is usually confirmed on a pulmonary function test, which is not always readily accessible. We aimed to develop a computationally lightweight handcrafted machine learning model for asthma detection based on cough sounds recorded using mobile phones. Toward this aim, we proposed a novel feature extractor based on a one-dimensional version of the published attractive-and-repulsive center-symmetric local binary pattern (1D-ARCSLBP), which we tested on a new cough sound dataset. We prospectively recorded cough sounds from 511 asthmatics and 815 non-asthmatic subjects (comprising mostly healthy volunteers), which yielded 1875 one-second cough sound segments for analysis. Our model comprised four steps: (i) preprocessing, in which speech signals and stop times (silent zones between coughs) were removed, leaving behind analyzable cough sound segments; (ii) feature extraction, in which tunable q-factor wavelet transformation was used to perform multilevel signal decomposition into wavelet subbands, allowing 1D-ARCSLBP to extract local low- and high-level features; (iii) feature selection, in which neighborhood component analysis was used to select the most discriminative features; and (iv) classification, in which a standard shallow cubic support vector machine was deployed to calculate binary classification results (asthma versus non-asthma) using tenfold and leave-one-subject-out cross-validations. Our model attained 98.24% and 96.91% accuracy rates with tenfold and leave-one-subject-out cross-validation strategies, respectively, and obtained a low-time complexity. The excellent results confirmed the feature extraction capability of 1D-ARCSLBP and the feasibility of the model being developed into a real-world application for asthma screening.

Abstract Engines are prone to various types of faults, and it is crucial to detect and indeed classify them accurately. However, manual fault type detection is time‐consuming and error‐prone. Automated fault type detection promises to reduce inter‐ and intra‐observer variability while ensuring time invariant attention during the observation duration. We have proposed an automated fault‐type detection model based on sound signals to realize these advantageous properties. We have named the detection model prism pattern network (PrismPatNet) to reflect the fact that our design incorporates a novel feature extraction algorithm that was inspired by a 3D prism shape. Our prism pattern model achieves high accuracy with low‐computational complexity. It consists of three main phases: (i) prism pattern inspired multilevel feature generation and maximum pooling operator, (ii) feature ranking and feature selection using neighbourhood component analysis (NCA), and (iii) support vector machine (SVM) based classification. The maximum pooling operator decomposes the sound signal into six levels. The proposed prism pattern algorithm extracts parameter values from both the signal itself and its decompositions. The generated parameter values are merged and fed to the NCA algorithm, which extracts 512 features from that input. The resulting feature vectors are passed on to the SVM classifier, which labels the input as belonging to 1 of 27 classes. We have validated our model with a newly collected dataset containing the sound of (1) a normal engine and (2) 26 different types of engine faults. Our model reached an accuracy of 99.19% and 98.75% using 80:20 hold‐out validation and 10‐fold cross‐validation, respectively. Compared with previous studies, our model achieved the highest overall classification accuracy even though our model was tasked with identifying significantly more fault classes. This performance indicates that our PrismPatNet model is ready to be installed in real‐world applications.

Abstract In the field of nanoscience, the scanning electron microscope (SEM) is widely employed to visualize the surface topography and composition of materials. In this study, we present a novel SEM image classification model called NFSDense201, which incorporates several key components. Firstly, we propose a unique nested patch division approach that divides each input image into four patches of varying dimensions. Secondly, we utilize DenseNet201, a deep neural network pretrained on ImageNet1k, to extract 2920 deep features from the last fully connected and global average pooling layers. Thirdly, we introduce an iterative neighborhood component analysis function to select the most discriminative features from the merged feature vector, which is formed by concatenating the four feature vectors extracted per input image. This process results in a final feature vector of optimal length 698. Lastly, we employ a standard shallow support vector machine classifier to perform the actual classification. To evaluate the performance of NFSDense201, we conducted experiments using a large public SEM image dataset. The dataset consists of 972, 162, 326, 4590, 3820, 3925, 4755, 181, 917, and 1624.jpeg images belonging to the following microstructural categories: “biological,” “fibers,” “film-coated surfaces,” “MEMS devices and electrodes,” “nanowires,” “particles,” “pattern surfaces,” “porous sponge,” “powder,” and “tips,” respectively. For both four-class and ten-class classification tasks, we evaluated NFSDense201 using subsets of the dataset containing 5080 and 21,272 images, respectively. The results demonstrate the superior performance of NFSDense201, achieving a four-class classification accuracy rate of 99.53% and a ten-class classification accuracy rate of 97.09%. These accuracy rates compare favorably against previously published SEM image classification models. Additionally, we report the performance of NFSDense201 for each class in the dataset.

Abstract Automated urine sediment analysis has become an essential part of diagnosing, monitoring, and treating various diseases that affect the urinary tract and kidneys. However, manual analysis of urine sediment is time-consuming and prone to human bias, and hence there is a need for an automated urine sediment analysis systems using machine learning algorithms. In this work, we propose Swin-LBP, a handcrafted urine sediment classification model using the Swin transformer architecture and local binary pattern (LBP) technique to achieve high classification performance. The Swin-LBP model comprises five phases: preprocessing of input images using shifted windows-based patch division, six-layered LBP-based feature extraction, neighborhood component analysis-based feature selection, support vector machine-based calculation of six predicted vectors, and mode function-based majority voting of the six predicted vectors to generate four additional voted vectors. Our newly reconstructed urine sediment image dataset, consisting of 7 distinct classes, was utilized for training and testing our model. Our proposed model has several advantages over existing automated urinalysis systems. Firstly, we used a feature engineering model that enables high classification performance with linear complexity. This means that it can provide accurate results quickly and efficiently, making it an attractive alternative to time-consuming and biased manual urine sediment analysis. Additionally, our model outperformed existing deep learning models developed on the same source urine sediment image dataset, indicating its superiority in urine sediment classification. Our model achieved 92.60% accuracy for 7-class urine sediment classification, with an average precision of 92.05%. These results demonstrate that the proposed Swin-LBP model can provide a reliable and efficient solution for the diagnosis, surveillance, and therapeutic monitoring of various diseases affecting the kidneys and urinary tract. The proposed model's accuracy, speed, and efficiency make it an attractive option for clinical laboratories and healthcare facilities. In conclusion, the Swin-LBP model has the potential to revolutionize urine sediment analysis and improve patient outcomes in the diagnosis and treatment of urinary tract and kidney diseases.

Abstract The number of Major Depressive Disorder (MDD) patients is rising rapidly these days following the incidence of COVID‐19 pandemic. It is challenging to detect MDD through personal interviews and by observing electroencephalogram (EEG) signals. Hence, an automated MDD detection system developed using deep learning techniques can help reduce the workload of clinicians by diagnosing MDD accurately. In this study, we have proposed a novel deep learning model based on Convolutional Neural Network (CNN) and spectrogram images. In this work, Short‐Time Fourier Transform (STFT) is first applied to the EEG signals to obtain spectrogram images of MDD patients and healthy subjects. These spectrogram images are then fed to the CNN model for automated detection of MDD patients and healthy subjects. The EEG signals used in this study were obtained from public database with 34 MDD patients and 30 healthy subjects. The highest classification accuracy, precision, sensitivity, specificity, and F1‐score of 99.58%, 99.40%, 99.70%, 99.48%, and 99.55% respectively were obtained with hold‐out validation. Our MDD detection model is highly accurate and needs to be validated with more diverse MDD database before it can be used in clinical settings. Also, we plan to use our developed prototype to detect depression using other physiological signals like electrocardiogram (ECG) and speech signals for accurate and faster diagnosis.

Abstract Electroencephalogram (EEG) signals have been generally utilized for diagnostic systems. Nowadays artificial intelligence-based systems have been proposed to classify EEG signals to ease diagnosis process. However, machine learning models have generally been used deep learning based classification model to reach high classification accuracies. This work focuses classification epilepsy attacks using EEG signals with a lightweight and simple classification model. Hence, an automated EEG classification model is presented. The used phases of the presented automated EEG classification model are (i) multileveled feature generation using one-dimensional (1D) octal-pattern (OP) and discrete wavelet transform (DWT). Here, main feature generation function is the presented octal-pattern. DWT is employed for level creation. By employing DWT frequency coefficients of the EEG signal is obtained and octal-pattern generates texture features from raw EEG signal and wavelet coefficients. This DWT and octal-pattern based feature generator extracts 128 × 8 = 1024 (Octal-pattern generates 128 features from a signal, 8 signal are used in the feature generation 1 raw EEG and 7 wavelet low-pass filter coefficients). (ii) To select the most useful features, neighborhood component analysis (NCA) is deployed and 128 features are selected. (iii) The selected features are feed to k nearest neighborhood classifier. To test this model, an epilepsy seizure dataset is used and 96.0% accuracy is attained for five categories. The results clearly denoted the success of the presented octal-pattern based epilepsy classification model.

Abstract Image classification (categorization) can be considered as one of the most breathtaking domains of contemporary research. Indeed, people cannot hide their faces and related lineaments since it is highly needed for daily communications. Therefore, face recognition is extensively used in biometric applications for security and personnel attendance control. In this study, a novel face recognition method based on perceptual hash is presented. The proposed perceptual hash is utilized for preprocessing and feature extraction phases. Discrete Wavelet Transform (DWT) and a novel graph based binary pattern, called quintet triple binary pattern (QTBP), are used. Meanwhile, the K-Nearest Neighbors (KNN) and Support Vector Machine (SVM) algorithms are employed for classification task. The proposed face recognition method is tested on five well-known face datasets: AT&T, Face94, CIE, AR and LFW. Our proposed method achieved 100.0% classification accuracy for the AT&T, Face94 and CIE datasets, 99.4% for AR dataset and 97.1% classification accuracy for the LFW dataset. The time cost of the proposed method isO(nlogn). The obtained results and comparisons distinctly indicate that our proposed has a very good classification capability with short execution time.

The perceptibility and capacity are two vital criteria of data hiding scheme. Concerning these criteria, data hiding algorithm used images as cover object based on the graph theory is proposed in this study. Images are quantised according to determined range and then quantised images are divided into n × n sized blocks. Each block is accepted as a graph and vertexes which have the same quantisation value are accepted as neighbours. Neighbourhood degrees of vertexes are calculated and indices of vertexes that have a neighbourhood degree over the threshold value are stored in the codebook. Pixel values indicated by these indices in the codebook are used for data hiding process. In this algorithm, there is no need for edge extraction because of hiding data to pixels containing vertexes having high neighbourhood degrees. The proposed method is compared with similar methods in literature in terms of the perceptibility and capacity. More successful results are provided than the others.