Bi-objective approach for computer-aided diagnosis of schizophrenia patients using fMRI data

  • Indranath Chatterjee
  • Manoj Agarwal
  • Bharti Rana
  • Navin Lakhyani
  • Naveen Kumar
Article

Abstract

Computer-aided diagnosis (CAD) of schizophrenia based on the analysis of brain images, captured using functional Magnetic Resonance Imaging (fMRI) technique, is an active area of research. The main problem lies in the identification of brain regions that contribute to differentiating between a healthy subject and a schizophrenia affected subject. The problem becomes complex due to the high dimensionality of the fMRI data on the one hand and the availability of data for only a small number of subjects on the other hand. In this paper, we propose a three-stage evolutionary based framework for feature selection. It comprises application of general linear model, followed by statistical hypothesis testing, and finally application of Non-dominated Sorting Genetic Algorithm (NSGA-II) to arrive at a small set of about fifty features. Experiments show that the feature set generated by the proposed approach yields accuracy as high as 99.5% in classifying fMRI dataset of healthy and schizophrenia subjects, and can identify the relevant brain regions that are affected in schizophrenia.

Keywords

Functional magnetic resonance imaging (fMRI) Schizophrenia Computer-aided diagnosis (CAD) Feature selection NSGA-II Bi-objective evolutionary algorithm 

Notes

Acknowledgements

We are thankful to Prof. R. K. Agrawal, School of Computer & Systems Sciences, Jawaharlal Nehru University, Delhi, India for his insightful comments. Indranath Chatterjee is thankful to the Council of Scientific & Industrial Research (CSIR), India for his research fellowship with grant number 09/045(1323)/2014-EMR-I. Naveen Kumar is thankful to University of Delhi for the research grant RC/2015/9677. Data used here for this study were downloaded from the Function BIRN Data Repository (http://fbirnbdr.birncommunity.org:8080/BDR/), i.e., Biomedical Informatics Research Network under the following support: for function data, U24-RR021992, Function BIRN and U24 GM104203, Bio-Informatics Research Network Coordinating Center (BIRN-CC). These data were obtained from the Function BIRN Data Repository, Project Accession Number 2007-BDR-6UHZ1.

Compliance with Ethical Standards

Conflict of interests

The authors declare that they have no conflict of interest.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of DelhiDelhiIndia
  2. 2.Department of Computer Science, Hans Raj CollegeUniversity of DelhiDelhiIndia
  3. 3.Department of MRISaral DiagnosticsNew DelhiIndia

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