A Comparative Analysis on Metaheuristic Algorithms Based Vision Transformer Model for Early Detection of Alzheimer's Disease
This work addresses early detection of Alzheimer's disease for older populations, but it appears incremental as it combines existing metaheuristic algorithms with a Vision Transformer model.
The paper tackled early detection of Alzheimer's disease by proposing a metaheuristic algorithms-based Vision Transformer model, achieving superior performance in accuracy, precision, recall, and F1-score on a sizable test dataset.
A number of life threatening neuro-degenerative disorders had degraded the quality of life for the older generation in particular. Dementia is one such symptom which may lead to a severe condition called Alzheimer's disease if not detected at an early stage. It has been reported that the progression of such disease from a normal stage is due to the change in several parameters inside the human brain. In this paper, an innovative metaheuristic algorithms based ViT model has been proposed for the identification of dementia at different stage. A sizeable number of test data have been utilized for the validation of the proposed scheme. It has also been demonstrated that our model exhibits superior performance in terms of accuracy, precision, recall as well as F1-score.