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.

ABSTRACT Recent trends in computational pathology favour increasingly complex deep learning architectures, raising the question of whether such complexity is necessary for routine diagnostic tasks. This study challenges this assumption through a comprehensive analysis of the relationship between model complexity, data pre‐processing, and performance across four fundamental digital pathology tasks: nuclei counting, steatosis quantification, glomeruli detection, and Ki67 proliferation index (PI) assessment. We evaluated five deep learning models of varying complexity (lightweight: MobileNetV2, U‐Net, and more complex: ConvNeXt, K‐Net, and Swin Transformer) combined with different image pre‐processing techniques. To evaluate model performance without extensive ground truth (GT) annotations, we introduced a validation strategy utilizing the relative absolute deviation (RAD) between network predictions and correlation of performance metrics. Our findings demonstrate that pre‐processing strategies, particularly stain normalization (NORM), can be more impactful than model complexity, reducing error rates by up to 50% compared to processing original (ORIG) images. With appropriate pre‐processing, lightweight models achieved comparable or superior results to complex models while reducing processing times by up to 40%. Only specific tasks involving complex morphological features, such as glomeruli detection, significantly benefited from more sophisticated architectures. This study provides an evidence‐based framework for selecting optimal model‐pre‐processing combinations in clinical settings, suggesting that investing in pre‐processing pipelines rather than model complexity may be more beneficial for routine computational pathology applications.

Color medical images introduce an additional confounding factor compared to conventional grayscale medical images: color variability. This variability can lead to inconsistent evaluation by clinicians and the misinterpretation or suboptimal learning process of automatic quantitative algorithms. To mitigate the potential negative consequences of color variability, several color normalization strategies have been developed, proving to be effective in standardizing image appearance. In this paper, we present a novel paradigm for color normalization using generative adversarial networks (GANs). Our method focuses on standardizing images in the field of digital pathology (stain normalization) and dermatology (color constancy), where high color variability is consistently observed. Specifically, we formulate the color normalization task as an image-to-image translation problem, ensuring a pixel-to-pixel correspondence between the original and normalized images. Our approach outperforms existing state-of-the-art methods in both the digital pathology and dermatology fields. Extensive validation using public datasets demonstrate the effectiveness of our color normalization results on entirely external test sets. Our framework exhibits strong generalization capability on unseen data, making it suitable for inclusion in the pipeline of automatic quantitative algorithms to reduce color variability and improve segmentation and/or classification performance. Lastly, we provide the source code of our models to encourage open science.

This study aims to address the need for reliable diagnosis of coronary artery disease (CAD) using artificial intelligence (AI) models. Despite the progress made in mitigating opacity with explainable AI (XAI) and uncertainty quantification (UQ), understanding the real-world predictive reliability of AI methods remains a challenge. In this study, we propose a novel indicator called the Spatial Uncertainty Estimator (SUE) to assess the prediction reliability of classification networks in practical Electrocardiography (ECG) scenarios. SUE quantifies the spatial overlap of critical Grad-CAM (Gradient-weighted Class Activation Mapping) features, offering a confidence score for predictions. To validate SUE, we designed a deep learning network that integrates Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) mechanisms for precise ECG signal classification of CAD. This network achieved high accuracy, sensitivity, and specificity rates of 99.6%, 99.8%, and 98.2%, respectively. During test time, SUE accurately distinguishes between correctly classified and misclassified ECG segments, demonstrating the superiority of the proposed network over existing methods. The study highlights the potential of combining XAI and UQ techniques to enhance ECG analysis. The evaluation of spatial overlap among discriminative features provides quantitative insights into the network's robustness, encompassing both current prediction accuracy and the repeatability of predictions.

The aim of this paper is to describe a novel and completely automated technique for carotid artery (CA) recognition, far (distal) wall segmentation, and intima-media thickness (IMT) measurement, which is a strong clinical tool for risk assessment for cardiovascular diseases. The architecture of completely automated multiresolution edge snapper (CAMES) consists of the following two stages: 1) automated CA recognition based on a combination of scale-space and statistical classification in a multiresolution framework and 2) automated segmentation of lumen-intima (LI) and media-adventitia (MA) interfaces for the far (distal) wall and IMT measurement. Our database of 365 B-mode longitudinal carotid images is taken from four different institutions covering different ethnic backgrounds. The ground-truth (GT) database was the average manual segmentation from three clinical experts. The mean distance ± standard deviation of CAMES with respect to GT profiles for LI and MA interfaces were 0.081 ± 0.099 and 0.082 ± 0.197 mm, respectively. The IMT measurement error between CAMES and GT was 0.078 ± 0.112 mm. CAMES was benchmarked against a previously developed automated technique based on an integrated approach using feature-based extraction and classifier (CALEX). Although CAMES underestimated the IMT value, it had shown a strong improvement in segmentation errors against CALEX for LI and MA interfaces by 8% and 42%, respectively. The overall IMT measurement bias for CAMES improved by 36% against CALEX. Finally, this paper demonstrated that the figure-of-merit of CAMES was 95.8% compared with 87.4% for CALEX. The combination of multiresolution CA recognition and far-wall segmentation led to an automated, low-complexity, real-time, and accurate technique for carotid IMT measurement. Validation on a multiethnic/multi-institutional data set demonstrated the robustness of the technique, which can constitute a clinically valid IMT measurement for assistance in atherosclerosis disease management.