CNN-Based Classification and Diagnosis of Alzheimer's from Baseline MRI Scans

The diagnosis of neurodegenerative diseases like Alzheimer's remains a medical challenge, relying heavily on physicians' interpretation of symptoms. Early detection and man-agement are critical for improving patient outcomes, but there is a lack of automated tools to assist in decision-making. This project aims to address this gap a framework for detecting Alzheimer's disease from MRI scans. A convolutional neural network (CNN) is used in the proposed method to classify images into four categories: No Dementia (ND), Very Mild Dementia (VMD), Mild Dementia (MD), and Moderate Dementia (MOD). Leveraging the proven capabilities of CNNs in medical image classification, a tailored architecture was designed and implemented. The model delivered strong performance, demonstrating its potential for early detection of Alzheimer's. This system could be used in clinical settings to assist physicians in identifying cases of Alzheimer's disease and improving diagnostic efficiency.