Brain tumor segmentation from MRI images using convolutional neural networks

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

Bibliografiset tiedot
Päätekijä:	Khan, Mushfiqur Rahman
Muut tekijät:	Khondaker, Arnisha
Aineistotyyppi:	Opinnäyte
Kieli:	English
Julkaistu:	Brac University 2024
Aiheet:	Bain tumor Convolutional Neural Network U-Net ResNet50 ResNext50 EfficientNetB7 Segmentation MRI image Neural networks (Computer science) Image processing > Digital techniques.
Linkit:	http://hdl.handle.net/10361/24177

id	10361-24177
record_format	dspace
spelling	10361-241772024-09-24T21:04:27Z Brain tumor segmentation from MRI images using convolutional neural networks Khan, Mushfiqur Rahman Khondaker, Arnisha Department of Computer Science and Engineering, Brac University Bain tumor Convolutional Neural Network U-Net ResNet50 ResNext50 EfficientNetB7 Segmentation MRI image Neural networks (Computer science) Image processing--Digital techniques. 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 29-33). Brain tumors result from the accumulation of abnormal cells inside the brain. The process of brain tumor segmentation plays a vital role in detecting early-stage brain tumors. There are several challenges in the tumor segmentation process due to the variations in size, location, and intensity of brain tissues. Traditional brain tumor segmentation methods are very time-consuming as segmentation is carried out manually. Automated segmentation methods are necessary for rapid diagnosis and treatment. In our paper, we highlight the complications surrounding a brain tumor and use an encoder-decoder-based approach of CNN algorithms to train segmentation models that will help identify and localize brain tumors with the utmost accuracy. We have used four CNN architectures to train our models, namely UNet, ResNet50, ResNext50, and EfficientNetB7. For our encoder-decoder models, we used ResNet50, ResNext50, and EfficientNetB7 as the encoder blocks of U-Net in three different models. Hence, we performed three experiments using these four architectures and compared their performance. We obtained the best results using the U-Net + EfficientNetB7 model. Mushfiqur Rahman Khan B.Sc. in Computer Science 2024-09-24T06:30:10Z 2024-09-24T06:30:10Z 2022 2022 Thesis ID 19241006 http://hdl.handle.net/10361/24177 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. 33 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Bain tumor Convolutional Neural Network U-Net ResNet50 ResNext50 EfficientNetB7 Segmentation MRI image Neural networks (Computer science) Image processing--Digital techniques.
spellingShingle	Bain tumor Convolutional Neural Network U-Net ResNet50 ResNext50 EfficientNetB7 Segmentation MRI image Neural networks (Computer science) Image processing--Digital techniques. Khan, Mushfiqur Rahman Brain tumor segmentation from MRI images using convolutional neural networks
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
author2	Khondaker, Arnisha
author_facet	Khondaker, Arnisha Khan, Mushfiqur Rahman
format	Thesis
author	Khan, Mushfiqur Rahman
author_sort	Khan, Mushfiqur Rahman
title	Brain tumor segmentation from MRI images using convolutional neural networks
title_short	Brain tumor segmentation from MRI images using convolutional neural networks
title_full	Brain tumor segmentation from MRI images using convolutional neural networks
title_fullStr	Brain tumor segmentation from MRI images using convolutional neural networks
title_full_unstemmed	Brain tumor segmentation from MRI images using convolutional neural networks
title_sort	brain tumor segmentation from mri images using convolutional neural networks
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/24177
work_keys_str_mv	AT khanmushfiqurrahman braintumorsegmentationfrommriimagesusingconvolutionalneuralnetworks
_version_	1814309078911942656

Brain tumor segmentation from MRI images using convolutional neural networks

Samankaltaisia teoksia