Early Schizophrenia Diagnosis with 3D Convolutional Neural Network
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
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Brac University
2021
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| Acceso en línea: | http://hdl.handle.net/10361/15005 |
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10361-15005 |
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10361-150052022-01-26T10:23:14Z Early Schizophrenia Diagnosis with 3D Convolutional Neural Network Ashraf, S.M. Nabil Oikko, Isbat Mashiat Saha, Chayan Anik, Md. Rakib Enam Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Deep Learning Convolutional Neural Networks Schizophrenia fMRI 3D Binary Classification Deep Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (page 52-58). The proper prediction of schizophrenia at an early stage can be very beneficial to those who are at risk of developing it at a severe stage later on. The early signs of schizophrenia include extreme reaction to criticism, staring at something without any expression, in ability to express any kinds of emotion, distancing from family members,unnatural way of speaking and later the severe signs include showing extreme anger, hallucination, strange behaviour etc. In order to tackle the problem of diagnosing schizophrenia, researchers try to extract patterns from neuroimaging data for which various statistical methods and machine learning algorithms have been explored in the clinical and research applications. In this paper, fMRI scans of subjects aged between 16 and 30 have been strictly pre processed and then passed into four different 3D CNN architectures to extract and learn features for the binary classification of schizophrenia. In order to improve performance, and prevent overfitting, we experimented with different optimizers, batch size and dropout rate while monitoring these model’s training and validation accuracy. Eventually we found the optimal set of hyperparameters which best fits these models according to a set of per formance metrics that we have chosen.We finally tested each of these models on the test dataset and compared the results to deduce the best model suited for our binary classifi cation problem. S.M. Nabil Ashraf Isbat Mashiat Oikko Chayan Saha Md. Rakib Enam Anik B. Computer Science 2021-09-14T06:30:05Z 2021-09-14T06:30:05Z 2021-06 Thesis http://hdl.handle.net/10361/15005 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 58 pages application/pdf Brac University |
| institution |
Brac University |
| collection |
Institutional Repository |
| language |
English |
| topic |
Deep Learning Convolutional Neural Networks Schizophrenia fMRI 3D Binary Classification Deep Learning |
| spellingShingle |
Deep Learning Convolutional Neural Networks Schizophrenia fMRI 3D Binary Classification Deep Learning Ashraf, S.M. Nabil Oikko, Isbat Mashiat Saha, Chayan Anik, Md. Rakib Enam Early Schizophrenia Diagnosis with 3D Convolutional Neural Network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. |
| author2 |
Chakrabarty, Amitabha |
| author_facet |
Chakrabarty, Amitabha Ashraf, S.M. Nabil Oikko, Isbat Mashiat Saha, Chayan Anik, Md. Rakib Enam |
| format |
Thesis |
| author |
Ashraf, S.M. Nabil Oikko, Isbat Mashiat Saha, Chayan Anik, Md. Rakib Enam |
| author_sort |
Ashraf, S.M. Nabil |
| title |
Early Schizophrenia Diagnosis with 3D Convolutional Neural Network |
| title_short |
Early Schizophrenia Diagnosis with 3D Convolutional Neural Network |
| title_full |
Early Schizophrenia Diagnosis with 3D Convolutional Neural Network |
| title_fullStr |
Early Schizophrenia Diagnosis with 3D Convolutional Neural Network |
| title_full_unstemmed |
Early Schizophrenia Diagnosis with 3D Convolutional Neural Network |
| title_sort |
early schizophrenia diagnosis with 3d convolutional neural network |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://hdl.handle.net/10361/15005 |
| work_keys_str_mv |
AT ashrafsmnabil earlyschizophreniadiagnosiswith3dconvolutionalneuralnetwork AT oikkoisbatmashiat earlyschizophreniadiagnosiswith3dconvolutionalneuralnetwork AT sahachayan earlyschizophreniadiagnosiswith3dconvolutionalneuralnetwork AT anikmdrakibenam earlyschizophreniadiagnosiswith3dconvolutionalneuralnetwork |
| _version_ |
1814309706967023616 |