Alzheimer's Disease Diagnosis from Brain MRI Using Deep Learning Techniques

One of the major causes behind memory loss is a neurological disorder called Alzheimer's Disease. As of right now, there is no cure for this disease that can reverse or stop the disease progression. The development of an efficient model with high precision is vital for timely detection of the disease. Patients with Alzheimer's Disease are identified using different noninvasive or minimal invasive biomarkers such as Brain imaging, Bio fluid analysis and emerging biomarkers. MR imaging is to be considered as one of the noninvasive biomarker to look for evidence of memory impairment. Sometimes these indicators could be inexact when used as ground truth marking for Alzheimer’s Disease detection prior patient suffers from irreversible memory loss, which is why researchers have proposed multiple computer-aided methods. In this paper, an architecture of basic deep learning approached has been proposed. First approach uses the Convolution Neural Network design, while the second approach uses the VGG16 model, that utilized on the ADNI dataset with 3 progression stages such as Cognitive Normal, Mild Cognitive Impairment & severe stage of Alzheimer's Disease. These methods achieve impressive accuracy rates of 97% and 98% in effectively identifying and categorizing the progression levels of alzheimer’s disease. This technique can significantly contribute to improve alzheimer’s diagnosis and aid physicians in enhancing diagnostic accuracy by leveraging advanced computational techniques.