Intelligence-based medicine最新文献_第3页

Improving glioma grade classification with a hybrid CNN-transformer model 用CNN-transformer混合模型改进胶质瘤分级

Intelligence-based medicine Pub Date : 2026-03-01 Epub Date: 2026-01-19 DOI: 10.1016/j.ibmed.2025.100334

Sreedevi Gutta, Shyam Sundhar Yathirajam

{"title":"Improving glioma grade classification with a hybrid CNN-transformer model","authors":"Sreedevi Gutta, Shyam Sundhar Yathirajam","doi":"10.1016/j.ibmed.2025.100334","DOIUrl":"10.1016/j.ibmed.2025.100334","url":null,"abstract":"<div><h3>Purpose</h3><div>Accurate glioma grading is critical for treatment planning and prognosis. While convolutional neural networks (CNNs) capture fine-grained local features and transformers model long-range dependencies, each alone has limitations. This work investigates whether a hybrid CNN–Transformer architecture can improve classification performance on multi-sequence MRI.</div></div><div><h3>Approach</h3><div>We evaluated ten machine learning/deep learning models, including a radiomics approach, simple convolutional neural network (CNN), long-short term memory (LSTM), ensemble model, ResNet-based transfer learning, Vision Transformers (ViT, DeiT-base, DeiT-small, and DeiT-tiny), and a hybrid model that combines CNN features with DeiT Tiny. Performance was assessed using accuracy, precision, recall, and F1-score. Model interpretability was explored with Grad-CAM, attention maps, and t-SNE feature visualizations.</div></div><div><h3>Results</h3><div>Transformer-based models consistently outperformed CNNs and recurrent architectures, with DeiT Tiny achieving the best standalone performance (F1 = 0.90). The hybrid CNN+DeiT Tiny achieved the highest overall performance (F1 = 0.96, precision = 0.95, recall = 0.97), while maintaining practical efficiency (13 ms/slice inference, 89 MB model size). Interpretability analyses showed that the hybrid model effectively integrates local and global features, and t-SNE confirmed strong feature separability between glioma grades.</div></div><div><h3>Conclusions</h3><div>Combining CNNs and transformers yields superior accuracy, generalization, and interpretability for glioma grading, while remaining computationally feasible for real-time use. The hybrid model's accuracy and efficiency can help make glioma grading more reliable and useful in real clinical practice.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100334"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146037646","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimization of dendritic neuron model framework through activation pairing for multidomain medical diagnosis 基于激活配对的多域医学诊断树突神经元模型框架优化

Intelligence-based medicine Pub Date : 2026-03-01 Epub Date: 2026-01-22 DOI: 10.1016/j.ibmed.2026.100354

Dimas Chaerul Ekty Saputra , Affifah Mutiara Pertiwi , Dimas Adiputra , Dyah Putri Rahmawati , Alfian Ma'arif , Iswanto Suwarno , Nia Maharani Raharja , Hari Maghfiroh , Michael Angello Qadosy Riyadi

{"title":"Optimization of dendritic neuron model framework through activation pairing for multidomain medical diagnosis","authors":"Dimas Chaerul Ekty Saputra , Affifah Mutiara Pertiwi , Dimas Adiputra , Dyah Putri Rahmawati , Alfian Ma'arif , Iswanto Suwarno , Nia Maharani Raharja , Hari Maghfiroh , Michael Angello Qadosy Riyadi","doi":"10.1016/j.ibmed.2026.100354","DOIUrl":"10.1016/j.ibmed.2026.100354","url":null,"abstract":"<div><div>Accurate and interpretable medical diagnosis remains a critical challenge due to the heterogeneous nature of clinical data and the limitations of conventional machine learning models in capturing complex nonlinear relationships. Dendritic neuron models (DNMs), inspired by biological neural processing, offer a promising alternative through localized nonlinear integration. Rather than introducing new dendritic architectures, this study presents a systematic and activation-aware analysis of existing dendritic neuron models to examine how activation function pairings and dendritic depth influence learning stability and classification performance. Three dendritic variants, namely the Standard DNM, Multi-Dendritic Neural Network (MDNN), and Multi-In and Multi-Out Dendritic Neuron Layer (MODN), are evaluated under multiple dendritic–somatic activation pairings using a unified gradient-based learning framework. Unlike prior studies that rely on metaheuristic optimization or task-specific tuning, gradient-based training is adopted to improve convergence efficiency and reproducibility. Experimental results on five heterogeneous medical datasets demonstrate consistently strong diagnostic performance, achieving accuracy above 90 % on anemia and breast cancer datasets and competitive results on heart disease, diabetes, and hepatitis. Overall, the study provides insight into the interaction between activation behavior and dendritic architecture, highlighting the importance of activation-aware modeling for biologically inspired medical diagnosis and establishing a foundation for efficient and deployable dendritic learning systems.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100354"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Contrasting deep learning audio models for direct respiratory insufficiency detection versus blood oxygen saturation estimation 对比深度学习音频模型用于直接呼吸功能不全检测与血氧饱和度估计

Intelligence-based medicine Pub Date : 2026-03-01 Epub Date: 2025-12-12 DOI: 10.1016/j.ibmed.2025.100331

Marcelo Matheus Gauy , Natália Hitomi Koza , Ricardo Mikio Morita , Gabriel Rocha Stanzione , Arnaldo Cândido Júnior , Larissa Cristina Berti , Anna Sara Shafferman Levin , Ester Cerdeira Sabino , Flaviane Romani Fernandes Svartman , Marcelo Finger

{"title":"Contrasting deep learning audio models for direct respiratory insufficiency detection versus blood oxygen saturation estimation","authors":"Marcelo Matheus Gauy , Natália Hitomi Koza , Ricardo Mikio Morita , Gabriel Rocha Stanzione , Arnaldo Cândido Júnior , Larissa Cristina Berti , Anna Sara Shafferman Levin , Ester Cerdeira Sabino , Flaviane Romani Fernandes Svartman , Marcelo Finger","doi":"10.1016/j.ibmed.2025.100331","DOIUrl":"10.1016/j.ibmed.2025.100331","url":null,"abstract":"<div><div>This work aims to investigate the strengths and limitations of non-invasive audio-based deep learning methods for the detection of respiratory conditions. We contrast the performance obtained in tasks such as the expert-centered respiratory insufficiency (RI) detection with easily measured blood oxygen saturation (SpO2) estimation. Several deep learning audio models have been recently proposed for RI detection via voice and speech analysis; these models have obtained an accuracy of 95% in general patients and 97.4% in COVID-19 patients. Here, we extend those results, refining several pretrained audio neural networks (CNN6, CNN10 and CNN14) and Masked Autoencoders (Audio-MAE) for RI detection, showing that some of these models achieve near perfect accuracy (99.9% on COVID RI and 98.6% on general RI). The models were pretrained on AudioSet resulting in improved performance, with transfer learning playing a key role in the prevention of overfitting. The near-perfect RI detection performance suggests that low-cost and automated methods could be developed for assisting patient triage. In parallel, this paper seeks to verify SpO2 estimation feasibility, so we perform a 92% SpO2-threshold binary classification using the same architectures. In contrast to our findings for RI, this model yielded an accuracy below 70% and MCC-correlation below 0.3, indicating both that SpO2 estimation solely from audio is unfeasible and the presence of multiple features in the audios which are useful for RI detection, but not for SpO2 estimation. We propose that this discrepancy demonstrates the limits of voice and speech biomarkers across different diagnostic tasks under current technologies.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100331"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145738925","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Radiological trends in convolutional neural networks for breast cancer diagnosis 卷积神经网络在乳腺癌诊断中的放射学趋势

Intelligence-based medicine Pub Date : 2026-03-01 Epub Date: 2025-11-27 DOI: 10.1016/j.ibmed.2025.100322

Ka Lee Li , Martin Ga Zen Tam , Sai Ka Li , Fatema Aftab

{"title":"Radiological trends in convolutional neural networks for breast cancer diagnosis","authors":"Ka Lee Li , Martin Ga Zen Tam , Sai Ka Li , Fatema Aftab","doi":"10.1016/j.ibmed.2025.100322","DOIUrl":"10.1016/j.ibmed.2025.100322","url":null,"abstract":"<div><div>Breast cancer remains a leading cause of cancer-related mortality worldwide, making early and accurate diagnosis crucial for patient outcomes. Future diagnosis may employ convolutional neural networks (CNNs), which have established themselves as powerful multi-layered artificial intelligence (AI) tools for computer vision tasks, with growing applications in breast cancer detection, diagnosis and classification. To provide insight into this field's intellectual, social, and conceptual knowledge structures, we conducted a bibliometric review of its 100 most-cited articles. The review looked at articles from January 1, 1995 to August 23, 2024. Our network analyses encourage increased inter-country collaboration. Thematic mapping highlights the increasing role of CNNs as foundational components in present and future AI applications. Multiple correspondence analyses track progress in diagnostic accuracy, system performance, and advanced classification techniques. Study design analyses suggest a need for future CNN research to be benchmarked against human readers and foster closer collaboration between technical and clinical researchers. In this bibliometric analysis, we summarise key contributions, examine emerging research trends, and provide an overview of the evolving landscape of CNN applications in breast cancer diagnostics.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100322"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep-lymph: An advanced deep learning framework for precision diagnosis of lymphoma from histopathological images deep -lymph：一个先进的深度学习框架，用于从组织病理学图像中精确诊断淋巴瘤

Intelligence-based medicine Pub Date : 2026-03-01 Epub Date: 2026-02-18 DOI: 10.1016/j.ibmed.2026.100347

Eram Mahamud , Md. Assaduzzaman , Nafiz Fahad , Md. Jakir Hossen

{"title":"Deep-lymph: An advanced deep learning framework for precision diagnosis of lymphoma from histopathological images","authors":"Eram Mahamud , Md. Assaduzzaman , Nafiz Fahad , Md. Jakir Hossen","doi":"10.1016/j.ibmed.2026.100347","DOIUrl":"10.1016/j.ibmed.2026.100347","url":null,"abstract":"<div><div>Deep learning models have shown great promise in medical image classification, but their lack of interpretability limits their adoption in clinical settings. This study addresses the need for explainable models in lymphoma diagnosis using an enhanced model within a transfer learning framework. To improve interpretability, we incorporated Explainable Artificial Intelligence (XAI) techniques, including SHAP, LIME, Grad-CAM, and Grad-CAM++, Occlusion Sensitivity Map to provide insights into the model's decision-making process. The model was trained on high-quality lymphoma imaging data, and preprocessing techniques such as Denoising, Contrast Limited Adaptive Histogram Equalization (CLAHE), and Gamma Correction were applied to improve image clarity. We conducted an ablation study to identify optimal parameters for the model. Our proposed model achieved an accuracy of 99.99%, with precision and recall rates of 100%, demonstrating its exceptional performance. SHAP and LIME helped in understanding the model's decisions, while Grad-CAM and Grad-CAM++ identified the crucial image features that influenced classification, enhancing transparency and trust in AI-assisted lymphoma diagnosis. This study contributes to advancing the use of deep learning in oncology, offering a reliable and interpretable tool for lymphoma detection.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100347"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-class Alzheimer’s disease (AD) classification using Swin Transformer wavelet and Gray Wolf Optimization (GWO) 基于Swin Transformer小波和灰狼优化（GWO）的阿尔茨海默病多分类

Intelligence-based medicine Pub Date : 2026-03-01 Epub Date: 2026-02-26 DOI: 10.1016/j.ibmed.2026.100362

Aida Rezaei Nejad , Faeze Sadat Sadati Salimi , Mahdi Hemmasian , Saeed Mirzaee , Khabiba Abdiyeva , Ramin Mousa , Saba Hesaraki

{"title":"Multi-class Alzheimer’s disease (AD) classification using Swin Transformer wavelet and Gray Wolf Optimization (GWO)","authors":"Aida Rezaei Nejad , Faeze Sadat Sadati Salimi , Mahdi Hemmasian , Saeed Mirzaee , Khabiba Abdiyeva , Ramin Mousa , Saba Hesaraki","doi":"10.1016/j.ibmed.2026.100362","DOIUrl":"10.1016/j.ibmed.2026.100362","url":null,"abstract":"<div><div>Alzheimer’s disease (AD) is a slow-growing neurological disorder that destroys human thought and consciousness. This disease directly affects the development of mental ability and neurocognitive function. The number of Alzheimer’s patients is increasing day by day, especially in the elderly over 60 years of age, and it gradually becomes a cause of their death. Machine learning (ML) and deep learning (DL) approaches have been developed in the literature to improve the diagnosis and classification of AD. Machine learning approaches have cumbersome feature selection. Deep learning has been used in recent research because it automatically selects features. This research aims to present a Swin Transformer wavelet for Alzheimer’s classification based on structural MRI images in two-class, three-class and four-class modes. The proposed approach uses wavelet fusion in the Swin Transformer network to extract features. The outputs of the modified capsule are fed into a wavelet as feature vectors. The wavelet is a relevant feature selector in the proposed model. The Gray Wolf Optimization (GWO) method was used to find the model’s hyperparameters. The proposed approach achieved an accuracy of 0.9812 in 4-class classification, 0.9980 in 3-class classification, and 1.0 in 2-class classification. In the studies conducted in this research, the Swin Transformer wavelet+GWO model is the heaviest model in terms of the evaluation criteria Parameters(10e6), GFlops, and Memory (GB). This is while the EfficientNet model is the lightest in these criteria.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100362"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147396426","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Attention-guided lightweight deep learning architecture for classification of polycystic ovary syndrome from ultrasound images 用于多囊卵巢综合征超声图像分类的注意力引导轻量级深度学习架构

Intelligence-based medicine Pub Date : 2026-03-01 Epub Date: 2026-02-20 DOI: 10.1016/j.ibmed.2026.100358

Shamik Tiwari , Amar Shukla , Akhilesh Kumar Sharma

引用次数: 0

Explainable stacked-ensemble prediction of ABL1 tyrosine-kinase inhibitor resistance: A metaheuristic-optimized pipeline ABL1酪氨酸激酶抑制剂耐药的可解释的堆叠系综预测：一个元启发式优化管道

Intelligence-based medicine Pub Date : 2026-03-01 Epub Date: 2025-12-30 DOI: 10.1016/j.ibmed.2025.100341

Faris Hassan , Mohanad A. Deif , Alaa Zaghloul , Rania Elgohary , Mohammad Khishe

{"title":"Explainable stacked-ensemble prediction of ABL1 tyrosine-kinase inhibitor resistance: A metaheuristic-optimized pipeline","authors":"Faris Hassan , Mohanad A. Deif , Alaa Zaghloul , Rania Elgohary , Mohammad Khishe","doi":"10.1016/j.ibmed.2025.100341","DOIUrl":"10.1016/j.ibmed.2025.100341","url":null,"abstract":"<div><div>Accurate identification of ABL1 tyrosine-kinase mutations associated with therapeutic resistance can support timely adjustment of anticancer regimens; however, tyrosine-kinase inhibitor (TKI) mutation datasets are typically small and strongly imbalanced, which can bias model training and inflate performance if data processing is not strictly separated between training and testing. This study proposes an end-to-end, leakage-controlled machine-learning framework for ABL1 TKI-resistance prediction, in which all data-driven operations including feature selection and Synthetic Minority Oversampling Technique (SMOTE) are performed within cross-validation training folds only, preventing information from validation folds or the test set from influencing model development. Multiple base learners were independently tuned using metaheuristic hyperparameter optimization and then integrated using a stacked-ensemble architecture to reduce overfitting and improve generalization. On a held-out test set, the final ensemble achieved 91.9% accuracy, 75.0% precision, 96.9% specificity, 60.0% sensitivity, 66.7% F1-score, 0.626 MCC, 0.938 AUROC, and 0.729 PR-AUC, showing only a modest decline relative to cross-validation estimates. Post-hoc interpretability with Shapley additive explanations (SHAP) highlighted binding-score terms, mutation physicochemical descriptors, ligand flexibility, and local mutation-environment features as the main contributors, consistent with established principles of protein–ligand recognition. Overall, the results support a methodologically disciplined and interpretable approach for mutation-level resistance prediction, while motivating external validation and downstream evaluation of clinical utility.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100341"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Capsule-augmented deep learning architectures for mental health detection from social media text 用于社交媒体文本心理健康检测的胶囊增强深度学习架构

Intelligence-based medicine Pub Date : 2026-03-01 Epub Date: 2025-11-24 DOI: 10.1016/j.ibmed.2025.100319

Faheem Ahmad Wagay, Jahiruddin

{"title":"Capsule-augmented deep learning architectures for mental health detection from social media text","authors":"Faheem Ahmad Wagay, Jahiruddin","doi":"10.1016/j.ibmed.2025.100319","DOIUrl":"10.1016/j.ibmed.2025.100319","url":null,"abstract":"<div><div>Mental health detection from social media text has attracted growing research attention due to the global rise in mental health concerns. Traditional deep learning models, such as Bidirectional Long Short-Term Memory (BiLSTM) networks and hybrid Convolutional BiLSTM (Conv-BiLSTM) architectures, have demonstrated strong performance in text classification tasks. However, these models often struggle to capture the hierarchical and spatial relationships that are intrinsic to linguistic data. To address this limitation, this study investigates the integration of capsule networks with BiLSTM and Conv-BiLSTM architectures for mental health detection. Leveraging a real-world Reddit corpus, we conduct extensive experiments comparing baseline BiLSTM and Conv-BiLSTM models with their capsule-enhanced counterparts. Furthermore, we explore the role of advanced loss functions, such as focal loss and contrastive loss, in addressing class imbalance and mitigating boundary blurring among semantically overlapping disorders. Our findings indicate that incorporating capsule layers significantly strengthens feature representation, leading to notable improvements in accuracy and F1-score across multiple mental health categories. The study focuses on six key disorders, including depression, anxiety, borderline personality disorder (BPD), and bipolar disorder. In addition, model interpretability is enhanced using Local Interpretable Model-agnostic Explanations (LIME), which highlights the critical linguistic features driving predictions, thereby improving transparency and reliability in mental health evaluations.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100319"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145926923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

STViTDA-Net: An explainable transformer-based framework with STGAN-ViT-MAE and deformable attention for multi-class skin cancer classification STViTDA-Net：一个可解释的基于变压器的框架，具有stgan - vita - mae和可变形的关注，用于多类别皮肤癌分类

Intelligence-based medicine Pub Date : 2026-03-01 Epub Date: 2026-01-22 DOI: 10.1016/j.ibmed.2026.100350

Ravula Jyothsna , K. Prasanna , U. Moulali , V. Surya Narayana Reddy , D. Ramya Krishna , T. Praveen Kumar

{"title":"STViTDA-Net: An explainable transformer-based framework with STGAN-ViT-MAE and deformable attention for multi-class skin cancer classification","authors":"Ravula Jyothsna , K. Prasanna , U. Moulali , V. Surya Narayana Reddy , D. Ramya Krishna , T. Praveen Kumar","doi":"10.1016/j.ibmed.2026.100350","DOIUrl":"10.1016/j.ibmed.2026.100350","url":null,"abstract":"<div><div>Skin cancer continues to pose a major global health challenge, and its early identification is essential for improving patient outcomes. Traditional diagnostic practices rely heavily on clinician expertise and manual interpretation of dermoscopic images, making the process subjective, inconsistent, and time-consuming. To address these limitations, this work introduces STViTDA-Net, an explainable transformer-based framework designed for fast, objective, and scalable multi-class skin cancer classification. The model integrates three key components: STGAN for class-balanced dermoscopic image augmentation, ViT-MAE for robust hierarchical feature learning through masked patch reconstruction, and a Deformable Attention Transformer Encoder that adaptively focuses on irregular lesion boundaries and subtle spatial variations. Preprocessing with Error Level Analysis (ELA) enhances fine-grained diagnostic cues, while Grad-CAM provides interpretable heatmaps that highlight the regions influencing the model's predictions. Unlike manual dermoscopic evaluation, STViTDA-Net performs end-to-end inference within milliseconds and delivers consistent, expert-independent predictions supported by visual explanations. When evaluated on the ISIC2019 dataset comprising nine lesion categories, the model achieves 99.35 % accuracy, 99.0 % precision, 99.5 % recall, 99.2 % F1-score, and 99.2 % AUC-ROC, surpassing existing CNN and transformer baselines. By unifying class-balanced augmentation, adaptive feature encoding, deformable attention, and explainable outputs, STViTDA-Net establishes a powerful and efficient solution for automated dermatological diagnosis.</div></div>","PeriodicalId":73399,"journal":{"name":"Intelligence-based medicine","volume":"13 ","pages":"Article 100350"},"PeriodicalIF":0.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0