NDLI: A Novel Approach for Computer Aided Diagnosis of Schizophrenia using Auditory Oddball Functional MRI

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A Novel Approach for Computer Aided Diagnosis of Schizophrenia using Auditory Oddball Functional MRI

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A Novel Approach for Computer Aided Diagnosis of Schizophrenia using Auditory Oddball Functional MRI

Content Provider	ACM Digital Library
Author	Juneja, Akanksha Rana, Bharti Agrawal, R. K.
Abstract	Schizophrenia is a serious mental illness that requires timely and accurate diagnosis. Functional magnetic resonance imaging (fMRI) helps in identifying variations in activation patterns of schizophrenia patients and healthy subjects. But, manual diagnosis using fMRI is cumbersome and prone to subjective errors. This has drawn the attention of pattern recognition and computer vision research community towards developing a reliable and efficient decision model for computer aided diagnosis (CAD) of schizophrenia. However, high dimensionality and limited availability of fMRI samples leads to curse-of-dimensionality which may deteriorate the performance of a decision model. In this research work, a combination of feature extraction and feature selection techniques is employed to obtain a reduced set of relevant features for differentiating schizophrenia patients from healthy subjects. A general linear model approach is used for feature extraction on pre-processed fMRI data. Further t-test based feature selection is employed to determine a subset of discriminative features which are used for learning a decision model using support vector machine. Experiments are carried out on two balanced and well-age matched datasets (acquired on 1.5 Tesla and 3 Tesla scanners) of auditory oddball task derived from a publicly available multisite FBIRN dataset. The performance is evaluated in terms of sensitivity, specificity and classification accuracy, and compared with two well-known existing approaches. Experimental results demonstrate that the proposed model outperforms the two existing approaches in terms of sensitivity, specificity and classification accuracy. With the proposed approach, the classification accuracy of 80.9% and 88.0% is achieved for 1.5 Tesla and 3 Tesla datasets respectively. In addition, the brain regions containing the discriminative features are identified which may be used as biomarkers for CAD of schizophrenia using fMRI.
Starting Page	1
Ending Page	6
Page Count	6
File Format	PDF
ISBN	9781450330619
DOI	10.1145/2683483.2683520
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-12-14
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Feature selection Functional magnetic resonance imaging Schizophrenia Feature extraction Computer aided diagnosis
Content Type	Text
Resource Type	Article

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