Glioma brain tumour classification and detection of magnetic resonance imaging using convolutional neural network / Syazani Adriana Abdul Ghaffar

In Malaysia, brain tumour is uncommon cancer in comparison to other types of cancer, only 1.95% of malignancies cases have been reported by the Malaysian Journal of Public Health Medicine (2017). There are numerous types of brain tumours have been identified, such as Glioma. This glioma abnormality...

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Main Author:	Abdul Ghaffar, Syazani Adriana
Format:	Thesis
Language:	English
Published:	2023
Subjects:	Medical technology
Online Access:	https://ir.uitm.edu.my/id/eprint/89007/1/89007.pdf
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id	my-uitm-ir.89007
record_format	uketd_dc
spelling	my-uitm-ir.890072024-03-19T07:07:25Z Glioma brain tumour classification and detection of magnetic resonance imaging using convolutional neural network / Syazani Adriana Abdul Ghaffar 2023 Abdul Ghaffar, Syazani Adriana Medical technology In Malaysia, brain tumour is uncommon cancer in comparison to other types of cancer, only 1.95% of malignancies cases have been reported by the Malaysian Journal of Public Health Medicine (2017). There are numerous types of brain tumours have been identified, such as Glioma. This glioma abnormality occurs in the brain and spinal cord and is one of the most common types of primary brain tumours. It is the most aggressive and fatal type of tumour. Magnetic Resonance Imaging is an effective noninvasive method to detect presence of brain tumour, but it has limitations. The problem that is addressed in this research is that the manual evaluation of detecting brain tumours consumes time and to able to classify brain tumour, feature extraction needed to be done but it is complex. Besides that, noise interference may affect tumour classification accuracy. This research uses Convolution Neural Network to classify and detect the MRI brain image. Hence the objective of this research is to design and develop a prototype of glioma brain tumour classification and detection of MRI brain images using CNN. Lastly, evaluation is done to test the accuracy, functionality, and usability of the proposed prototype and had achieved 99.00% accuracy, 100.00% precision, 98.00% recall. The proposed method of detection on MRI brain images accurately classifies and detect the image and achieving a great score of classification accuracy. With further extensive research, the system can be improved with detecting more classes of MRI brain images and able to detect the location of the abnormalities in the brain region. 2023 Thesis https://ir.uitm.edu.my/id/eprint/89007/ https://ir.uitm.edu.my/id/eprint/89007/1/89007.pdf text en public degree Universiti Teknologi MARA, Melaka College of Computing, Informatics and Mathematics
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
topic	Medical technology
spellingShingle	Medical technology Abdul Ghaffar, Syazani Adriana Glioma brain tumour classification and detection of magnetic resonance imaging using convolutional neural network / Syazani Adriana Abdul Ghaffar
description	In Malaysia, brain tumour is uncommon cancer in comparison to other types of cancer, only 1.95% of malignancies cases have been reported by the Malaysian Journal of Public Health Medicine (2017). There are numerous types of brain tumours have been identified, such as Glioma. This glioma abnormality occurs in the brain and spinal cord and is one of the most common types of primary brain tumours. It is the most aggressive and fatal type of tumour. Magnetic Resonance Imaging is an effective noninvasive method to detect presence of brain tumour, but it has limitations. The problem that is addressed in this research is that the manual evaluation of detecting brain tumours consumes time and to able to classify brain tumour, feature extraction needed to be done but it is complex. Besides that, noise interference may affect tumour classification accuracy. This research uses Convolution Neural Network to classify and detect the MRI brain image. Hence the objective of this research is to design and develop a prototype of glioma brain tumour classification and detection of MRI brain images using CNN. Lastly, evaluation is done to test the accuracy, functionality, and usability of the proposed prototype and had achieved 99.00% accuracy, 100.00% precision, 98.00% recall. The proposed method of detection on MRI brain images accurately classifies and detect the image and achieving a great score of classification accuracy. With further extensive research, the system can be improved with detecting more classes of MRI brain images and able to detect the location of the abnormalities in the brain region.
format	Thesis
qualification_level	Bachelor degree
author	Abdul Ghaffar, Syazani Adriana
author_facet	Abdul Ghaffar, Syazani Adriana
author_sort	Abdul Ghaffar, Syazani Adriana
title	Glioma brain tumour classification and detection of magnetic resonance imaging using convolutional neural network / Syazani Adriana Abdul Ghaffar
title_short	Glioma brain tumour classification and detection of magnetic resonance imaging using convolutional neural network / Syazani Adriana Abdul Ghaffar
title_full	Glioma brain tumour classification and detection of magnetic resonance imaging using convolutional neural network / Syazani Adriana Abdul Ghaffar
title_fullStr	Glioma brain tumour classification and detection of magnetic resonance imaging using convolutional neural network / Syazani Adriana Abdul Ghaffar
title_full_unstemmed	Glioma brain tumour classification and detection of magnetic resonance imaging using convolutional neural network / Syazani Adriana Abdul Ghaffar
title_sort	glioma brain tumour classification and detection of magnetic resonance imaging using convolutional neural network / syazani adriana abdul ghaffar
granting_institution	Universiti Teknologi MARA, Melaka
granting_department	College of Computing, Informatics and Mathematics
publishDate	2023
url	https://ir.uitm.edu.my/id/eprint/89007/1/89007.pdf
_version_	1794192186090192896

Glioma brain tumour classification and detection of magnetic resonance imaging using convolutional neural network / Syazani Adriana Abdul Ghaffar

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