DETECTION OF ALZHEIMER'S DISEASE USING DEEP LEARNING

Alzheimer’s Disease is a progressive neurological disorder that severely impacts memory, thinking, andbehavior, especially in the elderly population. Early diagnosis is essential to slow its progression andimprove the patient’s quality of life. In this study, a deep learning-based approach using the MobileNetV2architecture is proposed to classify brain MRI images into different stages of Alzheimer’s Disease. Themodel is trained on a preprocessed and augmented dataset to improve generalization and robustness. Aftertraining for 40 epochs, the model achieved a classification accuracy of 93.1%, indicating strong potential forclinical applications. A web-based interface is also developed using Flask, allowing users to upload MRIscans and receive real-time predictions. This research highlights the effectiveness of lightweightconvolutional neural networks in medical image analysis, especially in settings with limited computationalresources