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
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Online Access:http://eprints.utm.my/id/eprint/93056/1/HasanNabeelsaleemMSKE2020.pdf
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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
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