Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues

The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance I...

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Main Author:	Falah Ali, Wadah
Format:	Thesis
Language:	English English English
Published:	2019
Subjects:	QP351-495 Neurophysiology and neuropsychology
Online Access:	http://eprints.uthm.edu.my/679/1/24p%20WADAH%20FALAH%20ALI.pdf http://eprints.uthm.edu.my/679/2/WADAH%20FALAH%20ALI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/679/3/WADAH%20FALAH%20ALI%20WATERMARK.pdf
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spelling	my-uthm-ep.6792021-08-19T05:58:56Z Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues 2019-06 Falah Ali, Wadah QP351-495 Neurophysiology and neuropsychology The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI)and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate and etc. Especially, in this work MRI images are used to diagnose tumor in the brain. However, the huge amount of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular time. But it having some limitation accurate quantitative measurements is provided for limited number of images. Hence trusted and automatic classification scheme are essential to prevent the death rate of human. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection is proposed segment the Region Proposal Network (RPN) by Faster R-CNN algorithm. Here, the concept of transfer learning is used during training. The proposed system helps to predict the correct type of tumor with better accuracy about 99%. and classifying by using Convolutional Neural Networks (CNN). The deeper architecture design is performed by using small kernels. Experimental results show that the CNN archives rate of 98% accuracy with low complexity and compared with the all other state of arts methods. 2019-06 Thesis http://eprints.uthm.edu.my/679/ http://eprints.uthm.edu.my/679/1/24p%20WADAH%20FALAH%20ALI.pdf text en public http://eprints.uthm.edu.my/679/2/WADAH%20FALAH%20ALI%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/679/3/WADAH%20FALAH%20ALI%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QP351-495 Neurophysiology and neuropsychology
spellingShingle	QP351-495 Neurophysiology and neuropsychology Falah Ali, Wadah Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues
description	The brain tumors, are the most common and aggressive disease, leading to a very short life expectancy in their highest grade. Thus, treatment planning is a key stage to improve the quality of life of patients. Generally, various image techniques such as Computed Tomography (CT), Magnetic Resonance Imaging (MRI)and ultrasound image are used to evaluate the tumor in a brain, lung, liver, breast, prostate and etc. Especially, in this work MRI images are used to diagnose tumor in the brain. However, the huge amount of data generated by MRI scan thwarts manual classification of tumor vs non-tumor in a particular time. But it having some limitation accurate quantitative measurements is provided for limited number of images. Hence trusted and automatic classification scheme are essential to prevent the death rate of human. The automatic brain tumor classification is very challenging task in large spatial and structural variability of surrounding region of brain tumor. In this work, automatic brain tumor detection is proposed segment the Region Proposal Network (RPN) by Faster R-CNN algorithm. Here, the concept of transfer learning is used during training. The proposed system helps to predict the correct type of tumor with better accuracy about 99%. and classifying by using Convolutional Neural Networks (CNN). The deeper architecture design is performed by using small kernels. Experimental results show that the CNN archives rate of 98% accuracy with low complexity and compared with the all other state of arts methods.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Falah Ali, Wadah
author_facet	Falah Ali, Wadah
author_sort	Falah Ali, Wadah
title	Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues
title_short	Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues
title_full	Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues
title_fullStr	Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues
title_full_unstemmed	Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues
title_sort	advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Fakulti Kejuruteraan Elektrik dan Elektronik
publishDate	2019
url	http://eprints.uthm.edu.my/679/1/24p%20WADAH%20FALAH%20ALI.pdf http://eprints.uthm.edu.my/679/2/WADAH%20FALAH%20ALI%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/679/3/WADAH%20FALAH%20ALI%20WATERMARK.pdf
_version_	1747830660470407168

Advanced neural networking and classification techniques for human brain tissues diagnoses: segmenting healthy, cancer affected and edema brain tissues

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