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A deep learning approach to diagnosing schizophrenia
| Title: | A deep learning approach to diagnosing schizophrenia. |
 54 views 30 downloads |
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| Name(s): |
Barry, Justin, Author Valliyil Thankachan, Sharma, Committee Chair Gurupur, Varadraj, Committee CoChair Jha, Sumit Kumar, Committee Member Ewetz, Rickard, Committee Member University of Central Florida, Degree Grantor |
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| Type of Resource: | text | |
| Date Issued: | 2019 | |
| Publisher: | University of Central Florida | |
| Language(s): | English | |
| Abstract/Description: | In this article, the investigators present a new method using a deep learning approach to diagnose schizophrenia. In the experiment presented, the investigators have used a secondary dataset provided by National Institutes of Health. The aforementioned experimentation involves analyzing this dataset for existence of schizophrenia using traditional machine learning approaches such as logistic regression, support vector machine, and random forest. This is followed by application of deep learning techniques using three hidden layers in the model. The results obtained indicate that deep learning provides state-of-the-art accuracy in diagnosing schizophrenia. Based on these observations, there is a possibility that deep learning may provide a paradigm shift in diagnosing schizophrenia. | |
| Identifier: | CFE0007429 (IID), ucf:52737 (fedora) | |
| Note(s): |
2019-05-01 M.S. Engineering and Computer Science, Computer Science Masters This record was generated from author submitted information. |
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| Subject(s): | Schizophrenia -- fMRI -- Deep Learning -- Random Forest -- SVM -- Logistic Regression. | |
| Persistent Link to This Record: | http://purl.flvc.org/ucf/fd/CFE0007429 | |
| Restrictions on Access: | public 2019-05-15 | |
| Host Institution: | UCF |
