Document Type
Article
Publication Date
2015
DOI
10.1109/jbhi.2015.2429556
Publication Title
IEEE Journal of Biomedical and Health Informatics
Volume
19
Issue
5
Pages
1610-1616
Abstract
Accurate classification of Alzheimer's disease (AD) and its prodromal stage, mild cognitive impairment (MCI), plays a critical role in possibly preventing progression of memory impairment and improving quality of life for AD patients. Among many research tasks, it is of a particular interest to identify noninvasive imaging biomarkers for AD diagnosis. In this paper, we present a robust deep learning system to identify different progression stages of AD patients based on MRI and PET scans. We utilized the dropout technique to improve classical deep learning by preventing its weight coadaptation, which is a typical cause of overfitting in deep learning. In addition, we incorporated stability selection, an adaptive learning factor, and a multitask learning strategy into the deep learning framework. We applied the proposed method to the ADNI dataset, and conducted experiments for AD and MCI conversion diagnosis. Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 5.9% on average as compared to the classical deep learning methods.
Original Publication Citation
Li, F., Tran, L., Thung, K. H., Ji, S. W., Shen, D. G., & Li, J. (2015). A robust deep model for improved classification of AD/MCI patients. IEEE Journal of Biomedical and Health Informatics, 19(5), 1610-1616. doi:10.1109/jbhi.2015.2429556
Repository Citation
Li, Feng; Tran, Loc; Thung, Kim-Han; Ji, Shuiwang; Shen, Dinggang; and Li, Jiang, "A Robust Deep Model for Improved Classification of AD/MCI Patients" (2015). Electrical & Computer Engineering Faculty Publications. 144.
https://digitalcommons.odu.edu/ece_fac_pubs/144
Included in
Biology Commons, Computer Sciences Commons, Mathematics Commons
Comments
NOTE: This is the author's pre-print version of a work that was published in IEEE Journal of Biomedical and Health Informatics. The final version was published as:
Li, F., Tran, L., Thung, K. H., Ji, S. W., Shen, D. G., & Li, J. (2015). A robust deep model for improved classification of AD/MCI patients. IEEE Journal of Biomedical and Health Informatics, 19(5), 1610-1616. doi:10.1109/jbhi.2015.2429556
Available at: http:dx.doi.org/10.1109/jbhi.2015.2429556