Neural Network-Based Support System to Improve Alzheimer's Detection Using Magnetic Resonance Imaging

Abstract

Early and accurate detection of Alzheimer’s is crucial for the quality of life of patients and families. Given the limitations of traditional methods, neural networks offer a promising alternative. This study implemented a neural network- based system to analyze brain magnetic resonance imaging (MRI) scans and detect Alzheimer’s. Using the SCRUM methodology, data acquisition and preparation, network training, and system evaluation were managed. The Inception V3 model achieved 98% accuracy, outperforming other models. This efficacy suggests that convolutional neural networks can significantly improve early detection, reducing the time to diagnosis. The findings support the use of advanced artificial intelligence to improve clinical outcomes and enable more timely interventions in Alzheimer’s patients.