A HYPERTUNED CNN ALGORITHM FOR ALZHEIMER’S DISEASE DETECTION
Alzheimer's disease is the extremely popular cause of dementia that causes memory loss. People who have Alzheimer’s disease suffer from a disorder in neurodegenerative which leads to loss in many brain functions. Nowadays researchers prove that early diagnosis of the disease is the most crucial aspect to enhance the care of patients’ lives and enhance treatment. Traditional approaches for diagnosis of Alzheimer’s disease (AD) suffers from long time with lack both efficiency and the time it takes for learning and training. Lately, deep-learning-based approaches have been considered for the classification of neuroimaging data correlated to AD. In this proposed work, Alzheimer’s disease is predicted with some of the machine learning models. Then prediction of AD has done using Deep learning algorithms such as Hypertuned CNN and Hypertuned Inception. Experimental results have been recorded. Finally Comparison analysis is done on machine leaning and deep leaning models among which Hypertuned CNN shows high accuracy i.e., 99% compared to Hypertuned Inception and Machine learning models i.e., 91% and 83% respectively.
Alzheimer’s disease, ML Models, Hypertuned CNN, Hypertuned Inception V3, Deep learning