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research-article

Classification of Schizophrenia versus normal subjects using deep learning

Authors:

Priya Aggarwal,

Anubha GuptaAuthors Info & Claims

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

Article No.: 28, Pages 1 - 6

https://doi.org/10.1145/3009977.3010050

Published: 18 December 2016 Publication History

Abstract

Motivated by deep learning approaches to classify normal and neuro-diseased subjects in functional Magnetic Resonance Imaging (fMRI), we propose stacked autoencoder (SAE) based 2-stage architecture for disease diagnosis. In the proposed architecture, a separate 4-hidden layer autoencoder is trained in unsupervised manner for feature extraction corresponding to every brain region. Thereafter, these trained autoencoders are used to provide features on class-labeled input data for training a binary support vector machine (SVM) based classifier. In order to design a robust classifier, noisy or inactive gray matter voxels are filtered out using a proposed covariance based approach. We applied the proposed methodology on a public dataset, namely, 1000 Functional Connectomes Project Cobre dataset consisting of fMRI data of normal and Schizophrenia subjects. The proposed architecture is able to classify normal and Schizophrenia subjects with 10-fold cross-validation accuracy of 92% that is better compared to the existing methods used on the same dataset.

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Cited By

Fafrowicz MTutajewski MSieradzki IOchab JCeglarek-Sroka ALewandowska KMarek TSikora-Wachowicz BPodolak IOświęcimka P(2024)Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networksFrontiers in Neuroinformatics10.3389/fninf.2024.148036618Online publication date: 20-Dec-2024
https://doi.org/10.3389/fninf.2024.1480366
Gütlin DMcDermott HGrundei MAuksztulewicz R(2024)Model-Based Approaches to Investigating Mismatch Responses in SchizophreniaClinical EEG and Neuroscience10.1177/1550059424125391056:1(8-21)Online publication date: 15-May-2024
https://doi.org/10.1177/15500594241253910
Huang ZLiu RZhu ZTan K(2024)Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRIIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.322517935:6(8161-8175)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3225179
Show More Cited By

Index Terms

Classification of Schizophrenia versus normal subjects using deep learning
1. Applied computing
  1. Life and medical sciences
    1. Computational biology
      1. Imaging
2. Computing methodologies
  1. Machine learning
    1. Cross-validation

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Information & Contributors

Information

Published In

cover image ACM Other conferences

ICVGIP '16: Proceedings of the Tenth Indian Conference on Computer Vision, Graphics and Image Processing

December 2016

743 pages

ISBN:9781450347532

DOI:10.1145/3009977

Program Chairs:
Dhruv Batra
Georgia Tech
,
Michael Brown
York University, Canada
,
Vijay Natarajan
Indian Institute of Science, Bangalore

Copyright © 2016 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Google Inc.
QI: Qualcomm Inc.
Tata Consultancy Services
NVIDIA
MathWorks: The MathWorks, Inc.
Microsoft Research: Microsoft Research

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 18 December 2016

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Funding Sources

Ministry of Communication and IT, Govt. of India

Conference

ICVGIP '16

Sponsor:

QI
MathWorks
Microsoft Research

ICVGIP '16: Indian Conference on Computer Vision, Graphics and Image Processing

December 18 - 22, 2016

Assam, Guwahati, India

Acceptance Rates

ICVGIP '16 Paper Acceptance Rate 95 of 286 submissions, 33%;

Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

Fafrowicz MTutajewski MSieradzki IOchab JCeglarek-Sroka ALewandowska KMarek TSikora-Wachowicz BPodolak IOświęcimka P(2024)Classification of ROI-based fMRI data in short-term memory tasks using discriminant analysis and neural networksFrontiers in Neuroinformatics10.3389/fninf.2024.148036618Online publication date: 20-Dec-2024
https://doi.org/10.3389/fninf.2024.1480366
Gütlin DMcDermott HGrundei MAuksztulewicz R(2024)Model-Based Approaches to Investigating Mismatch Responses in SchizophreniaClinical EEG and Neuroscience10.1177/1550059424125391056:1(8-21)Online publication date: 15-May-2024
https://doi.org/10.1177/15500594241253910
Huang ZLiu RZhu ZTan K(2024)Multitask Learning for Joint Diagnosis of Multiple Mental Disorders in Resting-State fMRIIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.322517935:6(8161-8175)Online publication date: Jun-2024
https://doi.org/10.1109/TNNLS.2022.3225179
Sarkar JHajdu A(2024)Comparative Analysis of Deep Learning Methods for Schizophrenia Classification from fMRI Scans2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS)10.1109/CBMS61543.2024.00020(69-74)Online publication date: 26-Jun-2024
https://doi.org/10.1109/CBMS61543.2024.00020
Tripathi AAhmed RTiwari A(2024)Review of Deep Learning Techniques for Neurological Disorders DetectionWireless Personal Communications10.1007/s11277-024-11464-x137:2(1277-1311)Online publication date: 14-Jul-2024
https://doi.org/10.1007/s11277-024-11464-x
Yousefian AShayegh FMaleki Z(2023)Detection of autism spectrum disorder using graph representation learning algorithms and deep neural network, based on fMRI signalsFrontiers in Systems Neuroscience10.3389/fnsys.2022.90477016Online publication date: 2-Feb-2023
https://doi.org/10.3389/fnsys.2022.904770
Swathi NPrabha S(2023)Survey on Structural Neuro Imaging for the Identification of Brain Abnormalities in SchizophreniaCurrent Medical Imaging Reviews10.2174/221155520466622013111263919:2(115-125)Online publication date: Feb-2023
https://doi.org/10.2174/2211555204666220131112639
Sharma MPatel RGarg ASanTan RAcharya U(2023)Automated detection of schizophrenia using deep learning: a review for the last decadePhysiological Measurement10.1088/1361-6579/acb24d44:3(03TR01)Online publication date: 6-Mar-2023
https://doi.org/10.1088/1361-6579/acb24d
Chaki JWoźniak M(2023)Deep learning for neurodegenerative disorder (2016 to 2022): A systematic reviewBiomedical Signal Processing and Control10.1016/j.bspc.2022.10422380(104223)Online publication date: Feb-2023
https://doi.org/10.1016/j.bspc.2022.104223
Nallusamy KEaswarakumar K(2023)Classifying schizophrenic and controls from fMRI data using graph theoretic framework and community detectionNetwork Modeling Analysis in Health Informatics and Bioinformatics10.1007/s13721-023-00415-412:1Online publication date: 4-Apr-2023
https://doi.org/10.1007/s13721-023-00415-4
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