Early Alzheimer’s Diagnosis Using Neural Networks
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions worldwide, with early diagnosis being crucial for effective intervention. Traditional diagnostic methods are often time-consuming and prone to subjectivity, necessitating automated approaches for improved accuracy and efficiency. This project proposes a deep learning-based framework for early Alzheimer’s detection using a ResNet (Residual Network) model trained on MRI and PET brain scan images. The ResNet architecture is chosen for its ability to learn deep hierarchical features while mitigating vanishing gradient issues, enabling robust classification of Alzheimer’s stages. The dataset is preprocessed to enhance image quality, and the model is trained and evaluated using standard metrics such as accuracy, sensitivity, and specificity. Experimental results demonstrate the potential of deep learning in achieving high diagnostic accuracy, aiding clinicians in early detection and intervention. This work contributes to advancing AI-driven medical diagnostics, offering a scalable and reliable solution for Alzheimer’s screening.
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