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Chest x-ray image classification on common thorax diseases using GLCM and AlexNet deep features

Image processing has been progressing far in medical as it is one of the main techniques used in the development of medical imaging diagnosis system. Some of the medical imaging modalities are the Magnetic Resonance Imaging (MRI), Computed Tomography (CT) Scan, X-Ray and Ultrasound. The output from...

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Main Authors: Tengku Zainul Akmal, Tengku Afiah Mardhiah, Than, Joel Chia Ming, Abdullah, Haslailee, Mohd. Noor, Norliza
Format: Article
Language:English
Published: Penerbit UTHM 2019
Subjects:
T Technology (General)
Online Access:http://eprints.utm.my/id/eprint/89535/1/TengkuAfiahMardhiah2019_ChestXRayImageClassificationonCommon.pdf
http://eprints.utm.my/id/eprint/89535/
http://dx.doi.org/10.30880/ijie.2019.11.04.003
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spelling my.utm.895352021-02-22T06:08:11Z http://eprints.utm.my/id/eprint/89535/ Chest x-ray image classification on common thorax diseases using GLCM and AlexNet deep features Tengku Zainul Akmal, Tengku Afiah Mardhiah Than, Joel Chia Ming Abdullah, Haslailee Mohd. Noor, Norliza T Technology (General) Image processing has been progressing far in medical as it is one of the main techniques used in the development of medical imaging diagnosis system. Some of the medical imaging modalities are the Magnetic Resonance Imaging (MRI), Computed Tomography (CT) Scan, X-Ray and Ultrasound. The output from all of these modalities would later be reviewed by the expert for an accurate result. Ensemble methods in machine learning are able to provide an automatic detection that can be used in the development of computer aided diagnosis system which can aid the experts in making their diagnosis. This paper presents the investigation on the classification of fourteen thorax diseases using chest x-ray image from ChestX-Ray8 database using Grey Level Co-occurrence Matrix (GLCM) and AlexNet feature extraction which are process using supervised classifiers: Zero R, k-NN, Naïve Bayes, PART, and J48 Tree. The classification accuracy result indicates that k-NN classifier gave the highest accuracy compare to the other classifiers with 47.51% accuracy for GLCM feature extraction method and 47.18% for AlexNet feature extraction method. The result shows that number of data by class and multilabelled data will influence the classifcation method. Data using GLCM feature extraction method has higher classification accuracy compared to AlexNet and required less processing step. Penerbit UTHM 2019-09 Article PeerReviewed application/pdf en http://eprints.utm.my/id/eprint/89535/1/TengkuAfiahMardhiah2019_ChestXRayImageClassificationonCommon.pdf Tengku Zainul Akmal, Tengku Afiah Mardhiah and Than, Joel Chia Ming and Abdullah, Haslailee and Mohd. Noor, Norliza (2019) Chest x-ray image classification on common thorax diseases using GLCM and AlexNet deep features. International Journal of Integrated Engineering, 11 (4). pp. 21-30. ISSN 2229-838X http://dx.doi.org/10.30880/ijie.2019.11.04.003 DOI:10.30880/ijie.2019.11.04.003
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
language English
topic T Technology (General)
spellingShingle T Technology (General)
Tengku Zainul Akmal, Tengku Afiah Mardhiah
Than, Joel Chia Ming
Abdullah, Haslailee
Mohd. Noor, Norliza
Chest x-ray image classification on common thorax diseases using GLCM and AlexNet deep features
description Image processing has been progressing far in medical as it is one of the main techniques used in the development of medical imaging diagnosis system. Some of the medical imaging modalities are the Magnetic Resonance Imaging (MRI), Computed Tomography (CT) Scan, X-Ray and Ultrasound. The output from all of these modalities would later be reviewed by the expert for an accurate result. Ensemble methods in machine learning are able to provide an automatic detection that can be used in the development of computer aided diagnosis system which can aid the experts in making their diagnosis. This paper presents the investigation on the classification of fourteen thorax diseases using chest x-ray image from ChestX-Ray8 database using Grey Level Co-occurrence Matrix (GLCM) and AlexNet feature extraction which are process using supervised classifiers: Zero R, k-NN, Naïve Bayes, PART, and J48 Tree. The classification accuracy result indicates that k-NN classifier gave the highest accuracy compare to the other classifiers with 47.51% accuracy for GLCM feature extraction method and 47.18% for AlexNet feature extraction method. The result shows that number of data by class and multilabelled data will influence the classifcation method. Data using GLCM feature extraction method has higher classification accuracy compared to AlexNet and required less processing step.
format Article
author Tengku Zainul Akmal, Tengku Afiah Mardhiah
Than, Joel Chia Ming
Abdullah, Haslailee
Mohd. Noor, Norliza
author_facet Tengku Zainul Akmal, Tengku Afiah Mardhiah
Than, Joel Chia Ming
Abdullah, Haslailee
Mohd. Noor, Norliza
author_sort Tengku Zainul Akmal, Tengku Afiah Mardhiah
title Chest x-ray image classification on common thorax diseases using GLCM and AlexNet deep features
title_short Chest x-ray image classification on common thorax diseases using GLCM and AlexNet deep features
title_full Chest x-ray image classification on common thorax diseases using GLCM and AlexNet deep features
title_fullStr Chest x-ray image classification on common thorax diseases using GLCM and AlexNet deep features
title_full_unstemmed Chest x-ray image classification on common thorax diseases using GLCM and AlexNet deep features
title_sort chest x-ray image classification on common thorax diseases using glcm and alexnet deep features
publisher Penerbit UTHM
publishDate 2019
url http://eprints.utm.my/id/eprint/89535/1/TengkuAfiahMardhiah2019_ChestXRayImageClassificationonCommon.pdf
http://eprints.utm.my/id/eprint/89535/
http://dx.doi.org/10.30880/ijie.2019.11.04.003
_version_ 1692991793788878848
score 13.211869

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