A Robust Deep Model for Improved Classification of AD/MCI Patients

Feng Li, Loc Tran, Kim Han Thung, Shuiwang Ji, Dinggang Shen, Jiang Li

Research output: Contribution to journalArticlepeer-review

238 Citations (Scopus)


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 languageEnglish
Article number7101222
Pages (from-to)1610-1616
Number of pages7
JournalIEEE Journal of Biomedical and Health Informatics
Issue number5
Publication statusPublished - 2015 Sept 1

Bibliographical note

Publisher Copyright:
© 2013 IEEE.


  • Alzheimer's Disease
  • Deep Learning
  • Early Diagnosis
  • MRI
  • PET

ASJC Scopus subject areas

  • Health Information Management
  • Health Informatics
  • Electrical and Electronic Engineering
  • Computer Science Applications


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