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Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method

The disease, tuberculosis (TB) is a serious health concern as it primarily affects the lungs and can lead to fatalities. However, early detection and treatment can cure the disease. One potential method for detecting TB is using Computer Aided Diagnosis (CAD) systems, which can analyze Chest X-Ray I...

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Main Authors: Elashmawy, M.A., Elamvazuthi, I., Izhar, L.I., Paramasivam, S., Su, S.
Format: Article
Published: 2023
Online Access:http://scholars.utp.edu.my/id/eprint/37584/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170643161&doi=10.14569%2fIJACSA.2023.0140808&partnerID=40&md5=89f146e83a9a225b375fe0d7d78dae85
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spelling oai:scholars.utp.edu.my:375842023-10-13T13:00:35Z http://scholars.utp.edu.my/id/eprint/37584/ Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method Elashmawy, M.A. Elamvazuthi, I. Izhar, L.I. Paramasivam, S. Su, S. The disease, tuberculosis (TB) is a serious health concern as it primarily affects the lungs and can lead to fatalities. However, early detection and treatment can cure the disease. One potential method for detecting TB is using Computer Aided Diagnosis (CAD) systems, which can analyze Chest X-Ray Images (CXR) for signs of TB. This paper proposes a new approach for improving the performance of CAD systems by using a hybrid pre-processing method for Convolutional Neural Network (CNN) models. The goal of the research is to enhance the accuracy and Area Under Curve (AUC) of detection for TB in CXR images by combining two different pre-processing methods and multi-classifying different manifestations of the disease. The hypothesis is that this approach will result in more accurate detection of TB in CXR images. To achieve this, this research used augmentation and segmentation techniques to pre-process the CXR images before feeding them into a pre-trained CNN model for classification. The VGG16 model managed to achieve an AUC of 0.935, an accuracy of 90 and a 0.8975 F1-score with the proposed pre-processing method. © (2023), (Science and Information Organization). All Rights Reserved. 2023 Article NonPeerReviewed Elashmawy, M.A. and Elamvazuthi, I. and Izhar, L.I. and Paramasivam, S. and Su, S. (2023) Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method. International Journal of Advanced Computer Science and Applications, 14 (8). pp. 69-76. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170643161&doi=10.14569%2fIJACSA.2023.0140808&partnerID=40&md5=89f146e83a9a225b375fe0d7d78dae85 10.14569/IJACSA.2023.0140808 10.14569/IJACSA.2023.0140808 10.14569/IJACSA.2023.0140808
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Institutional Repository
url_provider http://eprints.utp.edu.my/
description The disease, tuberculosis (TB) is a serious health concern as it primarily affects the lungs and can lead to fatalities. However, early detection and treatment can cure the disease. One potential method for detecting TB is using Computer Aided Diagnosis (CAD) systems, which can analyze Chest X-Ray Images (CXR) for signs of TB. This paper proposes a new approach for improving the performance of CAD systems by using a hybrid pre-processing method for Convolutional Neural Network (CNN) models. The goal of the research is to enhance the accuracy and Area Under Curve (AUC) of detection for TB in CXR images by combining two different pre-processing methods and multi-classifying different manifestations of the disease. The hypothesis is that this approach will result in more accurate detection of TB in CXR images. To achieve this, this research used augmentation and segmentation techniques to pre-process the CXR images before feeding them into a pre-trained CNN model for classification. The VGG16 model managed to achieve an AUC of 0.935, an accuracy of 90 and a 0.8975 F1-score with the proposed pre-processing method. © (2023), (Science and Information Organization). All Rights Reserved.
format Article
author Elashmawy, M.A.
Elamvazuthi, I.
Izhar, L.I.
Paramasivam, S.
Su, S.
spellingShingle Elashmawy, M.A.
Elamvazuthi, I.
Izhar, L.I.
Paramasivam, S.
Su, S.
Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method
author_facet Elashmawy, M.A.
Elamvazuthi, I.
Izhar, L.I.
Paramasivam, S.
Su, S.
author_sort Elashmawy, M.A.
title Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method
title_short Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method
title_full Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method
title_fullStr Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method
title_full_unstemmed Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method
title_sort detection of tuberculosis based on hybridized pre-processing deep learning method
publishDate 2023
url http://scholars.utp.edu.my/id/eprint/37584/
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170643161&doi=10.14569%2fIJACSA.2023.0140808&partnerID=40&md5=89f146e83a9a225b375fe0d7d78dae85
_version_ 1781707928133173248
score 13.223943

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