Hybrid Machine Learning Application in Alzheimer’s Disease Diagnosis through Magnetic Resonance Images

The Alzheimer's Disease (AD) is a progressive incurable neurodegenerative disease and one of the most challenging health problems that scientists are facing since decades. Despite the current diagnostic approach for AD involving clinical, neuropsychological, and neuroimaging assessments, this conventional diagnosis may be impractical and time-consuming given the significant burden of the disease. Furthermore, the identification of diagnostic factors from brain images is critical, considering the subtle changes in the brain and the variability in visual interpretation among clinicians. A computer-aided diagnosis (CAD) system based on Artificial Intelligence (AI) technologies is considered as a potential tool to mitigate this problem and to assist medical professionals in decision-making, providing a more reliable automatic detection. Our research goal is to find the maximum accuracy rate of AI-based diagnostic classification using neuroimaging data. In this paper, a hybrid machine learning (ML) model is proposed on a problem of recognition of demented subjects from healthy ones in accurate manner using the three anatomical sections of structural Magnetic Resonance Imaging (sMRI) dataset. The model has achieved 91.6% classification accuracy.