Deep Transfer Learning Framework for Alzheimer’s Disease Classification with Explainable AI Insights

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that requires early and reliable diagnosis, especially in low-resource settings. This study proposes an explainable deep learning framework for multi-class classification of AD stages from MRI brain images. A publicly available four-class Mendeley MRI dataset (Non- Demented, Very Mild, Mild, and Moderate-Demented) was preprocessed through resizing, normalization, augmentation, and an 80/10/10 train–validation–test split. Four transfer learning models GoogLeNet, DenseNet121, ResNet101, and VGG16 were fine- tuned using the Adam optimizer and evaluated with accuracy, precision, recall, F1-score, AUC, and mean categorical hinge loss. Among all architectures, GoogLeNet achieved the best performance with 0.98 accuracy, macro-F1 of 0.98, AUC of 1.00, and the lowest hinge loss (0.048), clearly outperforming the other models. Explainable AI techniques, Grad-CAM and LIME, were applied to highlight discriminative brain regions, consistently focusing on clinically relevant structures such as the hippocampus, temporal lobe, and ventricles. These pictorial elucidations uphold the clinical suitability of the model’s decisions. All in all, the suggested GoogLeNet based architecture is a mixture of high strong interpretable diagnostic accuracy, proving that it can be a assistance screening device in MRI-based AD detection under data constrained clinical. such environments as Bangladesh.