A Hybrid of ResNext101_32x8d and Swin Transformer Networks with XAI for Alzheimer’s Disease Detection
Rattachement africain : Égypte, sa. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Medical images obtained from advanced imaging devices play a crucial role in supporting disease diagnosis and detection. Nevertheless, acquiring such images is often costly and storage-intensive, and it is time-consuming to diagnose individuals. The use of artificial intelligence (AI)-based automated diagnostic systems provides potential solutions to address the limitations of cost and diagnostic time. In particular, deep learning and explainable AI (XAI) techniques provide a reliable and robust approach to classifying medical images. This paper presents a hybrid model comprising two networks, ResNext101_32x8d and Swin Transformer to differentiate four categories of Alzheimer’s disease: no dementia, very mild dementia, mild dementia, and moderate dementia. The combination of the two networks is applied to imbalanced data, trained on 5120 MRI images, validated on 768 images, and tested on 512 other images. Grad-CAM and LIME techniques with a saliency map are employed to interpret the predictions of the model, providing transparent and clinically interpretable decision support. The proposed combination is realized through a TensorFlow framework, incorporating hyperparameter optimization and various data augmentation methods. The performance evaluation of the proposed model is conducted through several metrics, including the error matrix, precision recall (PR), receiver operating characteristic (ROC), accuracy, and loss curves. Experimental results reveal that the hybrid of ResNext101_32x8d and Swin Transformer achieved a testing accuracy of 98.83% with a corresponding loss rate of 0.1019. Furthermore, for the combination “ResNext101_32x8d + Swin Transformer”, the precision, F1-score, and recall were 99.39%, 99.15%, and 98.91%, respectively, while the area under the ROC curve (AUC) was 1.00, “100%”. The combination of proposed networks with XAI techniques establishes a unique contribution to advance medical AI systems and assist radiologists during Alzheimer’s disease screening of patients.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A Hybrid of ResNext101_32x8d and Swin Transformer Networks with XAI for Alzheimer’s Disease Detection
- Date Crossref
- 02/02/2026
- Éditeur
- MDPI AG
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Higher Institute of Engineering Égypte (code pays fourni par la source)Université ou école supérieure
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University of Business and Technology Electrical Engineering Department pays non établi dans la noticeUniversité ou école supérieure
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Ain Shams University Égypte (code pays fourni par la source)Université ou école supérieure
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Faculty of Computer Science and Engineering Department of Artificial Intelligence Engineering Égypte (pays nommé en fin d’affiliation)Université ou école supérieure
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Al-Madinah Higher Institute for Engineering and Technology Department of Electronics and Communications Engineering Giza, Égypte (pays nommé en fin d’affiliation)Structure de recherche
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Faculty of Computer and Information Sciences Department of Basic Sciences Cairo, Égypte (pays nommé en fin d’affiliation)Université ou école supérieure
Higher Institute of Engineering (Égypte), Electrical Engineering Department — University of Business and Technology et Ain Shams University (Égypte), avec 3 autres affiliations. Pays d’affiliation : Égypte.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.