The Science and Information (SAI) Organization
  • Home
  • About Us
  • Journals
  • Conferences
  • Contact Us

Publication Links

  • IJACSA
  • Author Guidelines
  • Publication Policies
  • Metadata Harvesting (OAI2)
  • Digital Archiving Policy
  • Promote your Publication

IJACSA

  • About the Journal
  • Call for Papers
  • Author Guidelines
  • Fees/ APC
  • Submit your Paper
  • Current Issue
  • Archives
  • Indexing
  • Editors
  • Reviewers
  • Apply as a Reviewer

IJARAI

  • About the Journal
  • Archives
  • Indexing & Archiving

Special Issues

  • Home
  • Archives
  • Proposals
  • Guest Editors

Future of Information and Communication Conference (FICC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Computing Conference

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Intelligent Systems Conference (IntelliSys)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact

Future Technologies Conference (FTC)

  • Home
  • Call for Papers
  • Submit your Paper/Poster
  • Register
  • Venue
  • Contact
  • Home
  • Call for Papers
  • Indexing
  • Submit your Paper
  • Guidelines
  • Fees
  • Current Issue
  • Archives
  • Editors
  • Reviewers
  • Subscribe

DOI: 10.14569/IJACSA.2023.0140808

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

Author 1: Mohamed Ahmed Elashmawy
Author 2: Irraivan Elamvazuthi
Author 3: Lila Iznita Izhar
Author 4: Sivajothi Paramasivam
Author 5: Steven Su

PDF

International Journal of Advanced Computer Science and Applications(IJACSA), Volume 14 Issue 8, 2023.

  • Abstract and Keywords
  • How to Cite this Article
  • {} BibTeX Source

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.

Keywords: Tuberculosis; CNN; pre-processing; CXR images; augmentation; segmentation

Mohamed Ahmed Elashmawy, Irraivan Elamvazuthi, Lila Iznita Izhar, Sivajothi Paramasivam and Steven Su, “Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method” International Journal of Advanced Computer Science and Applications(IJACSA), 14(8), 2023. http://dx.doi.org/10.14569/IJACSA.2023.0140808

@article{Elashmawy2023,
title = {Detection of Tuberculosis Based on Hybridized Pre-Processing Deep Learning Method},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2023.0140808},
url = {http://dx.doi.org/10.14569/IJACSA.2023.0140808},
year = {2023},
publisher = {The Science and Information Organization},
volume = {14},
number = {8},
author = {Mohamed Ahmed Elashmawy and Irraivan Elamvazuthi and Lila Iznita Izhar and Sivajothi Paramasivam and Steven Su}
}



Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

IJACSA

Upcoming Conferences

Future of Information and Communication Conference (FICC) 2024

4-5 April 2024

  • Berlin, Germany

Computing Conference 2024

11-12 July 2024

  • London, United Kingdom

IntelliSys 2023

7-8 September 2023

  • Amsterdam, The Netherlands

Future Technologies Conference (FTC) 2023

2-3 November 2023

  • San Francisco, United States
The Science and Information (SAI) Organization
BACK TO TOP

Computer Science Journal

  • About the Journal
  • Call for Papers
  • Submit Paper
  • Indexing

Our Conferences

  • Computing Conference
  • Intelligent Systems Conference
  • Future Technologies Conference
  • Communication Conference

Help & Support

  • Contact Us
  • About Us
  • Terms and Conditions
  • Privacy Policy

© The Science and Information (SAI) Organization Limited. All rights reserved. Registered in England and Wales. Company Number 8933205. thesai.org