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

Elashmawy, MA; Elamvazuthi, I; Izhar, LI; Paramasivam, S; Su, S

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

Elashmawy, MA Elamvazuthi, I Izhar, LI Paramasivam, S Su, S

Permalink

Publisher:: The Science and Information Organization
Publication Type:: Journal Article
Citation:: International Journal of Advanced Computer Science and Applications, 2023, 14, (8), pp. 69-76
Issue Date:: 2023-01-01

Open Access

Copyright Clearance Process

Recently Added
In Progress
Open Access

This item is open access.

Adobe PDF

Download Published versionAdobe PDF (647.47 kB)

View on publisher's site

View statistics

Full metadata record

Field	Value	Language
dc.contributor.author	Elashmawy, MA
dc.contributor.author	Elamvazuthi, I
dc.contributor.author	Izhar, LI
dc.contributor.author	Paramasivam, S
dc.contributor.author	Su, S https://orcid.org/0000-0002-5720-8852
dc.date.accessioned	2024-04-14T20:49:08Z
dc.date.available	2024-04-14T20:49:08Z
dc.date.issued	2023-01-01
dc.identifier.citation	International Journal of Advanced Computer Science and Applications, 2023, 14, (8), pp. 69-76
dc.identifier.issn	2158-107X
dc.identifier.issn	2156-5570
dc.identifier.uri	http://hdl.handle.net/10453/177901
dc.description.abstract	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.
dc.language	en
dc.publisher	The Science and Information Organization
dc.relation.ispartof	International Journal of Advanced Computer Science and Applications
dc.relation.isbasedon	10.14569/IJACSA.2023.0140808
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	0803 Computer Software, 1005 Communications Technologies
dc.subject.classification	46 Information and computing sciences
dc.title	Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method
dc.type	Journal Article
utslib.citation.volume	14
utslib.for	0803 Computer Software
utslib.for	1005 Communications Technologies
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2024-04-14T20:49:06Z
pubs.issue	8
pubs.publication-status	Published
pubs.volume	14
utslib.citation.issue	8

Abstract:

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.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/177901