An efficient approach for binary classification in brain tumor detection using convolutional neural network

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.

Détails bibliographiques
Auteurs principaux:	Islam, MD. Arman, Noshin, Sheikh Araf, Islam, MD. Robiul, Razy, MD. Farhan, Antara, Samiha
Autres auteurs:	Shakil, Arif
Format:	Thèse
Langue:	English
Publié:	Brac University 2023
Sujets:	CNN Brain tumor Data-sets Deep learning sMRI CAD Binary crossentropy Neural networks (Computer science)
Accès en ligne:	http://hdl.handle.net/10361/21881

id	10361-21881
record_format	dspace
spelling	10361-218812023-10-25T21:02:23Z An efficient approach for binary classification in brain tumor detection using convolutional neural network Islam, MD. Arman Noshin, Sheikh Araf Islam, MD. Robiul Razy, MD. Farhan Antara, Samiha Shakil, Arif Parvez, Mohammad Zavid Department of Computer Science and Engineering, Brac University CNN Brain tumor Data-sets Deep learning sMRI CAD Binary crossentropy Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 66-70). Brain tumor detection using Convolutional Neural Network (CNN) models with binary classification has significantly improved the reliability of medical imaging through Deep Learning. The purpose of this research is to develop a modified CNN model by altering the different layers and weight values of each node to attain similar performance statistics to widely accepted CNN models while maintaining runtime efficiency. The proposed CNN model incorporates binary cross entropy to analyze the training data and accurately identifies whether or not a certain structured magnetic resonance imaging (sMRI) picture contains a tumor. In comparison to existing pre-trained CNN models, this study aims to contribute to the computer-aided diagnostic (CAD) system by implementing the proposed model with a simplified time complexity. The model achieved an overall classification accuracy of 96.7% after extensive tweaking of the proprietary CNN architecture. The suggested system’s performance is also compared with other existing systems, and the study demonstrates that it performs on par with most of them. MD. Arman Islam Sheikh Araf Noshin MD. Robiul Islam MD. Farhan Razy Samiha Antara B.Sc. in Computer Science and Engineering 2023-10-25T04:42:53Z 2023-10-25T04:42:53Z 2022 2022-01 Thesis ID 19101639 ID 18101471 ID 18101272 ID 18101480 ID 18101129 http://hdl.handle.net/10361/21881 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. 70 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	CNN Brain tumor Data-sets Deep learning sMRI CAD Binary crossentropy Neural networks (Computer science)
spellingShingle	CNN Brain tumor Data-sets Deep learning sMRI CAD Binary crossentropy Neural networks (Computer science) Islam, MD. Arman Noshin, Sheikh Araf Islam, MD. Robiul Razy, MD. Farhan Antara, Samiha An efficient approach for binary classification in brain tumor detection using convolutional neural network
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
author2	Shakil, Arif
author_facet	Shakil, Arif Islam, MD. Arman Noshin, Sheikh Araf Islam, MD. Robiul Razy, MD. Farhan Antara, Samiha
format	Thesis
author	Islam, MD. Arman Noshin, Sheikh Araf Islam, MD. Robiul Razy, MD. Farhan Antara, Samiha
author_sort	Islam, MD. Arman
title	An efficient approach for binary classification in brain tumor detection using convolutional neural network
title_short	An efficient approach for binary classification in brain tumor detection using convolutional neural network
title_full	An efficient approach for binary classification in brain tumor detection using convolutional neural network
title_fullStr	An efficient approach for binary classification in brain tumor detection using convolutional neural network
title_full_unstemmed	An efficient approach for binary classification in brain tumor detection using convolutional neural network
title_sort	efficient approach for binary classification in brain tumor detection using convolutional neural network
publisher	Brac University
publishDate	2023
url	http://hdl.handle.net/10361/21881
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An efficient approach for binary classification in brain tumor detection using convolutional neural network

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