Automated Alzheimer Disease Detection Model by Inducing an Efficient Fusion Strategy for Image-Based Classification

This paper presents a new fully automated image based classification method to evaluate the neurodegenerative disease, by feature extraction from brain magnetic resonance (MR) images based on support vector machine (SVM). Neurodegenerative diseases involves large variety of mental disorders, the evaluation of disease is not particularly related to visual differences carried out by radiologists. Such analysis that may examines the disease, which may not give comprehensive results to disease. Hence by this paper we introduced general and special visualization software designed in Visual Basics 2010.Application will produce quantitative and clinical analysis of MR images of brain. We use the Alzheimer’s disease (AD) as the case study; the results are affected regions of AD, and a plotted graph for regions of interest and percentage of death of neurons. This is achieved by fusion strategy, which combines bottom-up information flow and top-down information flow. Bottom-up includes Multiscale analysis of features from different images, where as top-down includes learning phase and fusion problem. Finally difference in individuality of regions found by this approach is highly correlated to clinical studies of Alzheimer’s disease.