Alzheimer’s Disease Detection in Various Brain Anatomies Based on Optimized Vision Transformer

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder and a growing public health concern. Despite significant advances in deep learning for medical image analysis, early and accurate diagnosis of AD remains challenging. In this study, we focused on optimizing the training process of deep learning models by proposing an enhanced version of the Adam optimizer. The proposed optimizer introduces adaptive learning rate scaling, momentum correction, and decay modulation to improve convergence speed, training stability, and classification accuracy. We integrated the enhanced optimizer with Vision Transformer (ViT) and Convolutional Neural Network (CNN) architectures. The ViT-based model comprises a linear projection of image patches, positional encoding, a transformer encoder, and a Multi-Layer Perceptron (MLP) head with a Softmax classifier for multiclass AD classification. Experiments on publicly available Alzheimer’s disease datasets (ADNI-1 and ADNI-2) showed that the enhanced optimizer enabled the ViT model to achieve a 99.84% classification accuracy on Dataset-1 and 95.75% on Dataset-2, outperforming Adam, RMSProp, and SGD. Moreover, the optimizer reduced entropy loss and improved convergence stability by 0.8–2.1% across various architectures, including ResNet, RegNet, and MobileNet. This work contributes a robust optimizer-centric framework that enhances training efficiency and diagnostic accuracy for automated Alzheimer’s disease detection.