HIERARCHICAL FEATURE EXTRACTION FOR EARLY ALZHEIMER’S DISEASE DIAGNOSIS

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ISSN: 2349-6002 ESTD Year: 2014

UGC approved journal no 47859

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
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