Chest infection classification from X-ray images using enhanced multisource transfer learning with voting system

Chest infection is a major health threat in most regions of the world. It is claimed to be one of the top causes of postoperative death after fragility hip fractures, according to a study presented in 2011. With the invention of deep learning in machine learning, implementation in Computer Aided Dia...

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Main Author:	Chiok, Alice Wen-Xin
Format:	Thesis
Language:	English
Published:	2018
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/79229/1/ChiokAliceWenMFKE2018.pdf
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id	my-utm-ep.79229
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spelling	my-utm-ep.792292018-10-14T08:39:41Z Chest infection classification from X-ray images using enhanced multisource transfer learning with voting system 2018 Chiok, Alice Wen-Xin TK Electrical engineering. Electronics Nuclear engineering Chest infection is a major health threat in most regions of the world. It is claimed to be one of the top causes of postoperative death after fragility hip fractures, according to a study presented in 2011. With the invention of deep learning in machine learning, implementation in Computer Aided Diagnosis system which utilizes deep neural networks for learning, classification, generation and even clustering has allowed X-ray image classification to be more accurate. The improvement in medical image classification using transfer learning is further studied. In this thesis, a novel deep neural network model which is composed of two Convolutional Neural Networks (CNNs) with different depth of weight layers, where the prediction probabilities for all CNNs are fused to the voting system for chest X-ray image classification is proposed and presented. The performance and accuracy of several existing deep learning model are investigated and compared to the proposed model. The outcome of this work, we successfully classified chest infection in chest X-ray images using the proposed model with overall accuracy of 83.69%. 2018 Thesis http://eprints.utm.my/id/eprint/79229/ http://eprints.utm.my/id/eprint/79229/1/ChiokAliceWenMFKE2018.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Chiok, Alice Wen-Xin Chest infection classification from X-ray images using enhanced multisource transfer learning with voting system
description	Chest infection is a major health threat in most regions of the world. It is claimed to be one of the top causes of postoperative death after fragility hip fractures, according to a study presented in 2011. With the invention of deep learning in machine learning, implementation in Computer Aided Diagnosis system which utilizes deep neural networks for learning, classification, generation and even clustering has allowed X-ray image classification to be more accurate. The improvement in medical image classification using transfer learning is further studied. In this thesis, a novel deep neural network model which is composed of two Convolutional Neural Networks (CNNs) with different depth of weight layers, where the prediction probabilities for all CNNs are fused to the voting system for chest X-ray image classification is proposed and presented. The performance and accuracy of several existing deep learning model are investigated and compared to the proposed model. The outcome of this work, we successfully classified chest infection in chest X-ray images using the proposed model with overall accuracy of 83.69%.
format	Thesis
qualification_level	Master's degree
author	Chiok, Alice Wen-Xin
author_facet	Chiok, Alice Wen-Xin
author_sort	Chiok, Alice Wen-Xin
title	Chest infection classification from X-ray images using enhanced multisource transfer learning with voting system
title_short	Chest infection classification from X-ray images using enhanced multisource transfer learning with voting system
title_full	Chest infection classification from X-ray images using enhanced multisource transfer learning with voting system
title_fullStr	Chest infection classification from X-ray images using enhanced multisource transfer learning with voting system
title_full_unstemmed	Chest infection classification from X-ray images using enhanced multisource transfer learning with voting system
title_sort	chest infection classification from x-ray images using enhanced multisource transfer learning with voting system
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2018
url	http://eprints.utm.my/id/eprint/79229/1/ChiokAliceWenMFKE2018.pdf
_version_	1747818177610383360

Chest infection classification from X-ray images using enhanced multisource transfer learning with voting system

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