Optimizing a 3D convolutional neural network to detect Alzheimer's disease based on MRI.

Alzheimer's disease (AD) is a progressive neurological disorder that affects millions worldwide, leading to cognitive decline and memory impairment. Structural changes in the brain gradually impair cognitive functions, and by the time symptoms become evident, significant and often irreversible neuronal damage has already occurred. This makes early diagnosis critical, as timely intervention can help slow disease progression and improve patients' quality of life. Recent advancements in machine learning and neuroimaging have enabled early detection of AD using imaging data and computer-aided diagnostic systems. Deep learning, particularly with magnetic resonance imaging (MRI), has gained widespread recognition for its ability to extract high-level features by leveraging localized connections, weight sharing, and three-dimensional invariance. In this study, we present a 3d convolutional neural network (3D-CNN) designed to enhance classification accuracy using data from the latest version of the OASIS database (OASIS-3). Unlike traditional 2D approaches, our model processes full 3D MRI scans to preserve spatial information and prevent information loss during dimensionality reduction. Additionally, we applied advanced preprocessing techniques, including intensity normalization and noise reduction, to enhance image quality and improve classification performance. Our proposed 3D-CNN achieved an impressive classification accuracy of 91%, outperforming several existing models. These results highlight the potential of deep learning in developing more reliable and efficient diagnostic tools for early Alzheimer's detection, paving the way for improved clinical decision-making and patient outcomes.