Early Detection of Alzheimer's Disease Using Bottleneck Transformers

Abstract

Early detection of Alzheimer's disease (AD) and its prodromal state, mild cognitive impairment (MCI), is crucial for providing suitable treatment and preventing the disease from progressing. It can also aid researchers and clinicians in identifying early biomarkers and ministering new treatments that have been a subject of extensive research. The application of deep learning techniques on structural magnetic resonance imaging (MRI) has shown promising results in diagnosing the disease. In this research, the authors intend to introduce a novel approach of using an ensemble of the self-attention-based bottleneck transformers with a sharpness-aware minimizer for early detection of Alzheimer's disease. The proposed approach has been tested on the widely accepted ADNI dataset and evaluated using accuracy, precision, recall, F1 score, and ROC-AUC score as the performance metrics.