Machine Learning based Detection of Alzheimer’s disease in MRI images

Abstract

Alzheimer's disease (AD) is a debilitating neurological disorder that most commonly affects the elderly. Alzheimer's disease patients have substantial memory loss. The Memory loss is caused by atrophy in the hippocampus, amygdala, and other areas of the brain in Alzheimer's patients. Identification and categorization of Alzheimer's disease are considered challenging research subjects due to the vast number of Alzheimer's patients and the absence of effective diagnostic procedures. And also traditional identification of Alzheimer’s disease is taking more time consuming. In order to tackle this issue, we have to use the Artificial Intelligence technologies, which are driven by machine learning base AD classification and identification.
 The combined SIFT and SURF feature is used in this study to describe an AD Classification based on images. These combined feature parameters are then fed into a machine learning classifier for additional classification accuracy. There were three distinct machine learning classifiers compared to the system: SVM, DT and k-NN. For testing the proposed AD system, we have gathered a benchmark dataset that includes four categories: Mild, Moderate, Non Demented and Very Mild. The experimental results showed that SVM achieved higher accuracy than other classifiers.