Alzheimer's Disease (AD) Diagnosis from Brain MRI Image using Neural Network Algorithm

Abstract

Disorders of the brain are one of the most difficult diseases to cure because of their fragility, the difficulty of performing procedures, and the high costs. On the other hand, the surgery itself does not have to be effective because the results are uncertain. Adults who have hypertension, one of the most common brain illnesses, may have different degrees of memory problems and forgetfulness. Depending on each patient's situation and for these reasons, it's crucial to define memory loss, determine the patient's level of decline, and determine his brain MRI scans are used to identify Alzheimer's disease. This study discusses about the utilization of deep learning in Alzheimer's disease identification. The proposed approach is utilized to enhance patient care, lower expenses, and enable quick and accurate analysis in sizable investigations. In many disciplines, including medical image processing cutting-edge deep learning approaches have recently effectively proven performance at the level of a human. By analyzing brain MRI data, a deep convolutional network model is suggested for diagnosing Alzheimer's disease. Compared to other models, the proposed model performs better for early disease detection because it can recognize distinct phases.