Classification of chest diseases from x-ray on chexpert dataset

This work proposes a method to classify tuberculosis (TB) disease in a chest radiograph using convolutional neural network algorithms (CNN). The main contribution of this work is to detect and classify ‘TB’ disease in addition to other 5 different diseases. This is achieved by using a transfer learn...

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Main Author:	Saleem, Hasan Nabeel
Format:	Thesis
Language:	English
Published:	2020
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/93056/1/HasanNabeelsaleemMSKE2020.pdf
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id	my-utm-ep.93056
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spelling	my-utm-ep.930562021-11-07T06:00:40Z Classification of chest diseases from x-ray on chexpert dataset 2020 Saleem, Hasan Nabeel TK Electrical engineering. Electronics Nuclear engineering This work proposes a method to classify tuberculosis (TB) disease in a chest radiograph using convolutional neural network algorithms (CNN). The main contribution of this work is to detect and classify ‘TB’ disease in addition to other 5 different diseases. This is achieved by using a transfer learning technique that utilizes a pre-trained ‘CNN’ network to classify the ‘TB’ disease. A comprehensive verification using TensorFlow is carried out to train and validate the proposed technique. This work aimed to use different pre-trained models on the CheXpert dataset and compare the area under the curve ‘AUC’ between the ‘CNN’ models. From the simulation work, it was found that it can be possible to classify the ‘TB’ in addition to the other 5 diseases without having a high reduction in the accuracy of classifying the 5 diseases. The results confirm that transfer learning technique is superior to the other methods, which exhibit less time for training and validating the datasets, and have good performance. This work achieved a new state of the art for classifying 3 different diseases (Atelectasis, Edema, and Tuberculosis) with ‘AUC’ 0.912, 0.945 and 0.954 respectively. Also, this work achieved second-best performance for classifying Pleural Effusion and Consolidation diseases with ‘AUC’ 0.928 and 0.917 respectively. The method proposed in this work can be used for all types of classification diseases in chest radiograph because it can be easily implemented by using pre-trained networks. 2020 Thesis http://eprints.utm.my/id/eprint/93056/ http://eprints.utm.my/id/eprint/93056/1/HasanNabeelsaleemMSKE2020.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:135946 masters Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering Faculty of Engineering - School 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 Saleem, Hasan Nabeel Classification of chest diseases from x-ray on chexpert dataset
description	This work proposes a method to classify tuberculosis (TB) disease in a chest radiograph using convolutional neural network algorithms (CNN). The main contribution of this work is to detect and classify ‘TB’ disease in addition to other 5 different diseases. This is achieved by using a transfer learning technique that utilizes a pre-trained ‘CNN’ network to classify the ‘TB’ disease. A comprehensive verification using TensorFlow is carried out to train and validate the proposed technique. This work aimed to use different pre-trained models on the CheXpert dataset and compare the area under the curve ‘AUC’ between the ‘CNN’ models. From the simulation work, it was found that it can be possible to classify the ‘TB’ in addition to the other 5 diseases without having a high reduction in the accuracy of classifying the 5 diseases. The results confirm that transfer learning technique is superior to the other methods, which exhibit less time for training and validating the datasets, and have good performance. This work achieved a new state of the art for classifying 3 different diseases (Atelectasis, Edema, and Tuberculosis) with ‘AUC’ 0.912, 0.945 and 0.954 respectively. Also, this work achieved second-best performance for classifying Pleural Effusion and Consolidation diseases with ‘AUC’ 0.928 and 0.917 respectively. The method proposed in this work can be used for all types of classification diseases in chest radiograph because it can be easily implemented by using pre-trained networks.
format	Thesis
qualification_level	Master's degree
author	Saleem, Hasan Nabeel
author_facet	Saleem, Hasan Nabeel
author_sort	Saleem, Hasan Nabeel
title	Classification of chest diseases from x-ray on chexpert dataset
title_short	Classification of chest diseases from x-ray on chexpert dataset
title_full	Classification of chest diseases from x-ray on chexpert dataset
title_fullStr	Classification of chest diseases from x-ray on chexpert dataset
title_full_unstemmed	Classification of chest diseases from x-ray on chexpert dataset
title_sort	classification of chest diseases from x-ray on chexpert dataset
granting_institution	Universiti Teknologi Malaysia, Faculty of Engineering - School of Electrical Engineering
granting_department	Faculty of Engineering - School of Electrical Engineering
publishDate	2020
url	http://eprints.utm.my/id/eprint/93056/1/HasanNabeelsaleemMSKE2020.pdf
_version_	1747818632154447872

Classification of chest diseases from x-ray on chexpert dataset

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