HIERARCHICAL FEATURE EXTRACTION FOR EARLY ALZHEIMER’S DISEASE DIAGNOSIS

HIERARCHICAL FEATURE EXTRACTION FOR EARLY ALZHEIMER’S DISEASE DIAGNOSIS
Author(s):
Nishant Pawar, Raghavendra Pratap Singh, Muskan Bajpai, Prajaktha P Prabhu, Chethana V
Keywords:
Alzheimer’s, ADNI, fMRI, Deep-Learning, Feature extraction, Data Processing
Abstract
There are different stages of Alzheimer’s disease. In our paper we have proposed a method to detect those having mild cognitive impairment (MCI) which is at the early stage of Alzheimer’s disease (AD). We have proposed a novel voxel-based hierarchical feature extraction (VHFE) method for the early alzheimer’s disease diagnosis. Firstly, we have parcellate the whole brain into several regions of interest (ROIs). Then, the brain feature maps of each subject made up of the fetched voxels and are fed into the convolutional neural network (CNN) to learn the deeply hidden features of the brain. Finally, to test the efficiency of our proposed method, we test it with the subset of the database.
Article Details
Unique Paper ID: 157776 Publication Volume & Issue: Volume 9, Issue 8 Page(s): 118 - 121
Article Preview & Download

Share This Article

NCSEM 2024

National Conference on Sustainable Engineering and Management - 2024

Last Date: 15th March 2024

IJIRT.org enables door in research by providing high quality research articles in open access market.

Send us any query related to your research on editor@ijirt.org