Early Detection of Alzheimer's Disease using Bottleneck Transformers
This work addresses early diagnosis for patients and clinicians, but it appears incremental as it builds on existing deep learning methods for MRI analysis.
The researchers tackled early detection of Alzheimer's Disease and Mild Cognitive Impairment by proposing an ensemble of Bottleneck Transformers with a sharpness aware minimizer, achieving results evaluated using accuracy, precision, recall, F1 score, and ROC-AUC on the ADNI dataset.
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 to identify early biomarkers and minister 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, we 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.