Early Detection of Alzheimer's Disease using Bottleneck Transformers
- URL: http://arxiv.org/abs/2305.00923v1
- Date: Mon, 1 May 2023 16:17:52 GMT
- Title: Early Detection of Alzheimer's Disease using Bottleneck Transformers
- Authors: Arunima Jaiswal, Ananya Sadana
- Abstract summary: We 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.
- Score: 1.14219428942199
- License: http://creativecommons.org/licenses/by/4.0/
- 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 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.
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