poster

Diagnosing Schizophrenia: A Deep Learning Approach

Authors:

Justin Barry,

Srivathsan Srinivasagopalan,

Sharma V. Thankachan,

Varadraj GurupurAuthors Info & Claims

BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Page 549

https://doi.org/10.1145/3233547.3233658

Published: 15 August 2018 Publication History

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Abstract

This paper presents a new method for diagnosing schizophrenia using deep learning. This experiment used a secondary dataset supplied by the National Institute of Health. The experiment analyzes the dataset and identifies schizophrenia using traditional machine learning methods such as logistic regression, support vector machines, and random forest. Finally, a deep neural network with three hidden layers is applied to the dataset. The results show that the neural network model yielded the highest accuracy, suggesting that deep learning may be a feasible method for diagnosing schizophrenia.

References

[1]

Maarten PJ Kuenen, Massimo Mischi, and Hessel Wijkstra . 2011. Contrast-ultrasound diffusion imaging for localization of prostate cancer. IEEE transactions on medical imaging Vol. 30, 8 (2011), 1493--1502.

Google Scholar

[2]

Roshan J. Martis, Hong Lin, Varadraj P. Gurupur, and Steven L. Fernandes . 2017. Editorial: Frontiers in development of intelligent applications for medical imaging processing and computer vision. Computers in Biology and Medicine Vol. 89 (2017), 549 -- 550.

Crossref

Google Scholar

[3]

Michael J. Owen, Akira Sawa, and Preben B. Mortensen . 2016. Schizophrenia. The Lancet Vol. 388, 10039 (Jul 02 . 2016), 86--97. Copyright - Copyright Elsevier Limited Jul 2, 2016; Last updated - 2017--11--23; CODEN - LANCAO.

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Published In

BCB '18: Proceedings of the 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

August 2018

727 pages

ISBN:9781450357944

DOI:10.1145/3233547

General Chairs:
Amarda Shehu
George Mason University, USA
,
Cathy Wu
University of Delaware, USA
,
Program Chairs:
Christina Boucher
University of Florida, USA
,
Jing Li
Case Western Reserve University, USA
,
Hongfang Liu
Mayo Clinic, USA
,
Mihai Pop
University of Maryland, USA

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Association for Computing Machinery

New York, NY, United States

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Published: 15 August 2018

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SIGBio

BCB '18: 9th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics

August 29 - September 1, 2018

DC, Washington, USA

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BCB '18 Paper Acceptance Rate 46 of 148 submissions, 31%;

Overall Acceptance Rate 254 of 885 submissions, 29%

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fMRI based computer aided diagnosis of schizophrenia using fuzzy kernel feature extraction and hybrid feature selection

The distinguishing intrinsic brain circuitry in treatment-naïve first-episode schizophrenia: Ensemble learning classification

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