Comparative study of X-ray and CT scan images for the detection of COVID-19 using 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.

Bibliographic Details
Main Authors:	Niloy, Ahashan Habib, Shiba, Shammi Akhter, Fahim, S.M. Farah Al, Faria, Faizun Nahar, Rahman, Md. Jamilur
Other Authors:	Parvez, Mohammad Zavid
Format:	Thesis
Language:	English
Published:	Brac University 2021
Subjects:	Confusion matrix Pneumonia CT scan X-ray Convolutional Neural Network Deep learning Machine learning COVID-19 COVID-19 (Disease)
Online Access:	http://hdl.handle.net/10361/15147

id	10361-15147
record_format	dspace
spelling	10361-151472022-01-26T10:19:58Z Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning Niloy, Ahashan Habib Shiba, Shammi Akhter Fahim, S.M. Farah Al Faria, Faizun Nahar Rahman, Md. Jamilur Parvez, Mohammad Zavid Department of Computer Science and Engineering, Brac University Confusion matrix Pneumonia CT scan X-ray Convolutional Neural Network Deep learning Machine learning COVID-19 COVID-19 (Disease) 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 42-54). 45 pages Coronavirus 2019 (in short, COVID-19), originated in the Wuhan province of China in December 2019, has been declared a global pandemic by WHO in March 2020. Since its inception, it’s rapid spread among nations had initially collapsed the world economy and the increasing death-pool created a strong fear among people as the virus spread through human contact. Initially doctors struggled to diagnose the increasing number of patients as there was less availability of testing kits and failed to treat people efficiently which ultimately led to the collapse of the health sector of several countries. To help doctors primarily diagnose the virus, researchers around the world have come up with some radiology imaging techniques using the Convo lutional Neural Network (CNN). While some of them worked on x-ray images and some others on CT scan images, none worked on both the image types. Thus there’s no way to know which image works better for a particular model. This, therefore, insisted us to perform a comparison between x-ray and CT scan images. Thus we came up with a novel CNN model named CoroPy which works for both the image types and shows that in 2 classes (normal and covid), CT scan images show a better accuracy and it is 99.17% whereas it is 95.73% for x-ray images. However, in the case of 3 classes (normal, covid and viral pneumonia), x-ray images show a better accuracy and it is 92.45% whereas it is 68.81% for CT scan images. Ahashan Habib Niloy Shammi Akhter Shiba S.M. Farah Al Fahim Faizun Nahar Faria Md. Jamilur Rahman B. Computer Science 2021-10-06T04:39:54Z 2021-10-06T04:39:54Z 2021 2015-08 Thesis ID: 17301004 ID:18201124 ID:17201151 ID: 17201003 ID: 17101291 http://hdl.handle.net/10361/15147 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. application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Confusion matrix Pneumonia CT scan X-ray Convolutional Neural Network Deep learning Machine learning COVID-19 COVID-19 (Disease)
spellingShingle	Confusion matrix Pneumonia CT scan X-ray Convolutional Neural Network Deep learning Machine learning COVID-19 COVID-19 (Disease) Niloy, Ahashan Habib Shiba, Shammi Akhter Fahim, S.M. Farah Al Faria, Faizun Nahar Rahman, Md. Jamilur Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning
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	Parvez, Mohammad Zavid
author_facet	Parvez, Mohammad Zavid Niloy, Ahashan Habib Shiba, Shammi Akhter Fahim, S.M. Farah Al Faria, Faizun Nahar Rahman, Md. Jamilur
format	Thesis
author	Niloy, Ahashan Habib Shiba, Shammi Akhter Fahim, S.M. Farah Al Faria, Faizun Nahar Rahman, Md. Jamilur
author_sort	Niloy, Ahashan Habib
title	Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning
title_short	Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning
title_full	Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning
title_fullStr	Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning
title_full_unstemmed	Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning
title_sort	comparative study of x-ray and ct scan images for the detection of covid-19 using deep learning
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15147
work_keys_str_mv	AT niloyahashanhabib comparativestudyofxrayandctscanimagesforthedetectionofcovid19usingdeeplearning AT shibashammiakhter comparativestudyofxrayandctscanimagesforthedetectionofcovid19usingdeeplearning AT fahimsmfarahal comparativestudyofxrayandctscanimagesforthedetectionofcovid19usingdeeplearning AT fariafaizunnahar comparativestudyofxrayandctscanimagesforthedetectionofcovid19usingdeeplearning AT rahmanmdjamilur comparativestudyofxrayandctscanimagesforthedetectionofcovid19usingdeeplearning
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Comparative study of X-ray and CT scan images for the detection of COVID-19 using deep learning

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