Early Detection of Alzheimer’s Disease Through Deep Learning Techniques Applied to Neuroimaging Data

Abstract

Alzheimer’s Disease (AD) is a progressive neurodegenerative disorder that affects millions worldwide and poses significant challenges for early diagnosis. Timely and accurate identification of AD is crucial for effective intervention and disease management. In this study, we propose a deep learning-based framework that leverages convolutional neural networks (CNNs) and transfer learning techniques to analyze structural magnetic resonance imaging (sMRI) data for early detection of Alzheimer’s Disease. The proposed model was trained and validated on a benchmark neuroimaging dataset, demonstrating strong classification performance in differentiating between AD, mild cognitive impairment (MCI), and healthy control (HC) groups. Experimental results show that the deep learning model outperforms traditional machine learning approaches in terms of accuracy, sensitivity, specificity, and AUC. This research underscores the potential of deep learning models in neuroimaging-based diagnosis and highlights their role in aiding clinical decision-making for neurodegenerative disorders.