Alzheimer’s Disease Detection System

Abstract

Alzheimer’s disease affects memory, thinking ability, and behavior, and over time it be comes difficult for patients to manage daily activities. Because of this, identifying the disease at an earlier stage is considered important. The traditional forms of detection usually use manual MRI data analysis and clinical examination. These methods take too much time and may produce inaccurate results. This project introduces an AI-assisted Alzheimer’s disease stage detection system based on the MRI images of the brain and a mobile-based application to interact with users. The deep learning ResNet50 architec ture is used to categorize MRI scans into stages of Alzheimer’s disease. The model is deployed as a FastAPI backend which interacts with a React Native application so that users can upload MRI scans and get predictions of the stage immediately. In addition to the prediction, the application features cognitive and memory enhancing games which are aimed at evaluating and training the mental performance, and an administration dashboard to monitor and manage the system and data. Firebase is used to provide secure user authentication, cloud-based storage of prediction results and user activity logs. This system introduces a convenient, interactive solution to the problem of detecting and learning about the Alzheimer’s disease at an early age through combining the capabilities of artificial intelligence, medical imaging, and cognitive engagement.