Application of deep learning in MRI classification of Schizophrenia

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.

Bibliografiske detaljer
Main Authors:	Joyee, Ramisa Fariha, Rodoshi, Lamia Hasan, Nadia, Yasmin
Andre forfattere:	Bin Ashraf, Faisal
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2024
Fag:	Schizophrenia Deep learning (DL) Neuro-image MRI Computer aided diagnosis Neuro-psychiatric disease DNN CNN SVM RNN COBRE NUSDAST Deep Learning
Online adgang:	http://hdl.handle.net/10361/22719

id	10361-22719
record_format	dspace
spelling	10361-227192024-05-05T21:04:45Z Application of deep learning in MRI classification of Schizophrenia Joyee, Ramisa Fariha Rodoshi, Lamia Hasan Nadia, Yasmin Bin Ashraf, Faisal Rahman, Md. Shahriar Department of Computer Science and Engineering, Brac University Schizophrenia Deep learning (DL) Neuro-image MRI Computer aided diagnosis Neuro-psychiatric disease DNN CNN SVM RNN COBRE NUSDAST Deep Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 44-47). In today’s world, when people are suffering from complex brain diseases, MRI has been playing a very significant part in understanding brain functionalities and its abnormalities. Deep learning has been recently used for the analysis of MRI, fMRI, structural MRI etc. and through this, we have achieved better performance than traditional computer-aided diagnosis for brain disorders. However, similar compo sition of brain diseases makes it hard to find out and differentiate the accuracy of exact disease from the acquired neuroimaging data. Accordingly, in this paper, a multi channel 2D CNN based architecture was implemented on COBRE dataset 1 which presents a significantly high accuracy over some models. Our modified multichannel 2D CNN architecture achieves around 97% accuracy which improves our classification performance. Furthermore, the paper discusses the boundaries of existing studies, the DL methods and present future possible directions. Ramisa Fariha Joyee Lamia Hasan Rodoshi Yasmin Nadia B.Sc. in Computer Science and Engineering 2024-05-05T05:17:30Z 2024-05-05T05:17:30Z 2023 2023-01-23 Thesis ID: 19301250 ID: 19301248 ID: 19301241 http://hdl.handle.net/10361/22719 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. 47 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Schizophrenia Deep learning (DL) Neuro-image MRI Computer aided diagnosis Neuro-psychiatric disease DNN CNN SVM RNN COBRE NUSDAST Deep Learning
spellingShingle	Schizophrenia Deep learning (DL) Neuro-image MRI Computer aided diagnosis Neuro-psychiatric disease DNN CNN SVM RNN COBRE NUSDAST Deep Learning Joyee, Ramisa Fariha Rodoshi, Lamia Hasan Nadia, Yasmin Application of deep learning in MRI classification of Schizophrenia
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2023.
author2	Bin Ashraf, Faisal
author_facet	Bin Ashraf, Faisal Joyee, Ramisa Fariha Rodoshi, Lamia Hasan Nadia, Yasmin
format	Thesis
author	Joyee, Ramisa Fariha Rodoshi, Lamia Hasan Nadia, Yasmin
author_sort	Joyee, Ramisa Fariha
title	Application of deep learning in MRI classification of Schizophrenia
title_short	Application of deep learning in MRI classification of Schizophrenia
title_full	Application of deep learning in MRI classification of Schizophrenia
title_fullStr	Application of deep learning in MRI classification of Schizophrenia
title_full_unstemmed	Application of deep learning in MRI classification of Schizophrenia
title_sort	application of deep learning in mri classification of schizophrenia
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22719
work_keys_str_mv	AT joyeeramisafariha applicationofdeeplearninginmriclassificationofschizophrenia AT rodoshilamiahasan applicationofdeeplearninginmriclassificationofschizophrenia AT nadiayasmin applicationofdeeplearninginmriclassificationofschizophrenia
_version_	1814309313391362048

Application of deep learning in MRI classification of Schizophrenia

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