Early Detection of Alzheimer's Disease Using Bottleneck Transformers

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