Identification and Stratification of Alzheimer’s Disease by Machine Learning

K. Jayamani, B. Gayathri, T. Harshavardhini and M. Deepa

For the past decade, the diagnosis of Alzheimer’s disease (AD) has become the biggest challenging problems in medical fields. In this paper, a region masking which is used for new segmentation method for selecting the useful properties of affected parts in the human brain for improving the accuracy of diagnosis for AD. In this proposed method features of collected data set, which can increase the accuracy of classification using SVM. Furthermore, the feature extraction is performed by GLCM and LBP. Also the different features are used to analyze AD. In this paper, the dataset will be discussed about normal and AD subjects. From the observed results, it significantly improves the accuracy of the diagnosis of AD when compared with previous methods.

Volume 12 | 05-Special Issue

Pages: 969-976

DOI: 10.5373/JARDCS/V12SP5/20201843