An efficient multiclass classifier for classification of Alzheimer's disease/mild cognitive impairment/Normal subjects
Abstract
Typically, in sparse representation‐based classifiers, the weight associated with each training sample is ignored, resulting in reduced accuracy. Moreover, individual binary classifiers solved a multiclass problem. It requires more time as multiple runs are needed to compute the accuracy. In this paper, we propose a novel optimal sparse representation‐based classifier. It solves the ternary classification problem with improved accuracy in a single run. The ternary classification considers Alzheimer's disease versus mild cognitive impairment versus normal control in a single run. A two‐stage sparse representation model is used to design the proposed classifier. To update the weight coefficients, we suggest a regularized Levenberg–Marquardt learning. It allows selecting a subset of significant training samples. To determine the appropriate subset size, we investigate an objective function in terms of classification accuracy. For optimization, we suggest a hybrid particle swarm optimization–squirrel search technique. The experiment conducted on the Alzheimer's Disease Neuroimaging Initiative database shows our method outperforms other state‐of‐the‐art methods in terms of computation time and accuracy. The use of different training–testing partition ratios makes the proposed method immune to biased results, overfitting, and underfitting difficulties. Moreover, results are obtained from 100 iterations to confirm its stability. The suggested model may be helpful for further research in medical image analysis.
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Sep 24, 2021
- Source ID
- 10.1002/ima.22656
Entities
People
- Ajith Abraham
- Lingraj Dora
- Rutuparna Panda
- Sanjay Agrawal
Organizations
- Alzheimer's Disease Neuroimaging Initiative
- Canadian Institutes of Health Research
- National Institute of Biomedical Imaging and Bioengineering
- National Institute on Aging
- National Institutes of Health
- Northern California Institute for Research and Education
- United States Department of Defense
- Veer Surendra Sai University of Technology