Texture Features Based Hybrid Multi Support Vector Machine Model for the diagnosis of Alzheimer's disease through Brain MRI Images

Alzheimer's Disease (AD) is a complex neurodegenerative disorder that severely affects cognitive functions and poses significant challenges in early and accurate diagnosis. In recent years, machine learning techniques have shown remarkable potential in medical image analysis, particularly when applied to Brain MRI Images for AD diagnosis. This study proposes a novel approach utilizing a Texture Features based Hybrid Multi-Support Vector Machine Model to enhance the precision and early detection of Alzheimer's disease through Brain MRI Images. The proposed model combines the strength of texture features extracted from Brain MRI Images with a Hybrid Multi- Support Vector Machine framework. Texture features capture subtle patterns and structural variations within brain images, making them valuable in distinguishing different stages of Alzheimer's disease. By leveraging the optimization capabilities of the Hybrid Multi-Support Vector Machine, the model effectively handles high-dimensional data and complex patterns, contributing to improved diagnostic accuracy. To evaluate the model's performance, a comprehensive dataset of Brain MRI Images, encompassing AD patients, Mild Cognitive Impairment cases, and non-AD individuals, was employed. The experimental results demonstrate the effectiveness of the proposed Texture Features based Hybrid Multi-Support Vector Machine Model in accurately classifying Alzheimer's disease cases, outperforming traditional diagnostic methods.