A Hybrid Architecture of CNN–Swin-T Integrated with Attention Mechanism and Explainable AI for Alzheimer’s Disease Classification

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that requires early and accurate diagnosis to improve patient outcomes. In this paper, an attention-enhanced hybrid deep learning (DL) framework is proposed that combines Convolutional Neural Network (CNN) and Swin Transformer (Swin-T) architectures for multi-class Alzheimer’s classification. The proposed model integrates an attention mechanism to enhance feature representation and improve classification performance. Experiments are conducted on a dataset containing three classes: Mild Demented, Very Mild Demented, and Non-Demented. To improve the model’s generalization, data augmentation techniques are applied to enhance the model’s performance. Additionally, three explainable artificial intelligence (XAI) techniques are employed, including Grad-CAM++, Integrated Gradients, and Saliency maps, to interpret the model’s predictions and to provide visual insights into decision-making processes. The proposed attention-enhanced hybrid CNN–Swin-T model achieves a testing accuracy of 99.92% and reaches 99.71%, 99.73%, and 99.72%, for precision, recall, and F1-score, respectively. The hybrid CNN–Swin-T with attention outperforms three implemented models: baseline CNN, standalone Swin-T, and hybrid CNN–Swin-T. The explainability results validate the proposed model’s focus on relevant regions, increasing trust in automated diagnosis systems. Finally, a comparative analysis with an ablation study is presented to demonstrate that the integration of the attention mechanism with a hybrid CNN–Swin-T architecture leads to the highest performance and more reliable predictions compared to the other three models.