Alzheimer's disease classification using a hybrid deep learning approach with multi-layer U-net segmentation and XAI driven analysis.

Alzheimer's disease (AD) is a neurodegenerative illness causing a significant decrease in cognitive function, and early, accurate diagnosis is of great therapeutic and diagnostic value. Currently, there is promising potential for applying various types of artificial intelligence techniques, such as enhanced models of deep learning, for classifying Alzheimer's disease. Therefore, this study proposes an Outline of deep learning to classify Alzheimer's disease with segmentation using the Multi-Layer U-Net and a hybrid classification approach combining multi-scale EfficientNet with SVM. The proposed methodology consists of a four-phase process: (1) Whole brain segmentation, (2) Gray matter segmentation using multi-layer U-Net segmentation, (3) Feature extraction using Multi-Scale Efficient Net with SVM for classification, and (4) XAI (explainable AI) techniques by integrating Saliency Map Quantitative Analysis for increased clinical trustworthiness and model interpretability. It is found that the experiment results provide promising classification performance for three classes - Alzheimer's Disease (AD), Mild Cognitive Impairment (MCI) and Cognitively Normal (CN) with an overall accuracy of 97.78% ± 0.54%, precision of 97.18% ± 1.14% (AD), 97.78% ± 0.29% (CN) and 97.03% ± 1.10% (MCI), recall of 97.90% ± 0.77% (AD), 97.49% ± 1.34% (CN) and 97.25% ± 0.99% (MCI), and F1 score of 97.74% ± 0.63% (AD), 97.78% ± 0.79% (CN), and 97.54% ± 0.69%(MCI). The results obtained underscore the elegance of the proposed approach in correctly classifying Alzheimer's disease stages. Future work will evaluate the model on publicly accessible Alzheimer's disease MRI datasets and incorporate advanced XAI techniques for increased interpretability and diagnostic reliability. The work focuses on Human Health.