Deep learning to predict Pulmonary Tuberculosis from Lung Posterior Chest Radiographs

  • hanan sharif department of computer science lahore leads university
  • faisal rehman department of computer science lahore leads university
  • naveed riaz school of computer science national university of science and technology islamabad
  • awais salman qazi Department of Computer Science, University of Management and Technology, Lahore
  • Rana Mohtasham Aftab
  • muhammad husnain department of computer science lahore leads university

Abstract

Tuberculosis is one of the most dangerous health conditions on the globe. As it affects the human body, tuberculosis is an infectious illness. According to the World Health Organization, roughly 1.7 million individuals get TB throughout the course of their lifetimes. Pakistan ranks fifth among high-burden nations and is responsible for 61% of the TB burden within the WHO Eastern Mediterranean Region. Various methods and procedures exist for the early identification of TB. However, all methods and techniques have their limits. The bulk of currently known approaches for detecting TB rely on model-based segmentation of the lung. The primary purpose of the proposed study is to identify pulmonary TB utilising chest X-ray (Poster Anterior) lung pictures processed using image processing and machine learning methods. The recommended study introduces a unique model segmentation strategy for TB identification. For classification, CNN, Google Net, and other systems based on deep learning are used. On merged datasets, the best accuracy attained by the suggested method utilising Google Net was 89.58 percent. The recommended study will aid in the detection and accurate diagnosis of TB. 

Published
2022-11-03
How to Cite
sharif, H., rehman, faisal, riaz, naveed, qazi, awais salman, Aftab, R. M., & hussain , muhammad. (2022). Deep learning to predict Pulmonary Tuberculosis from Lung Posterior Chest Radiographs. Lahore Garrison University Research Journal of Computer Science and Information Technology, 6(04), 16-22. https://doi.org/10.54692/lgurjcsit.2022.06004383
Section
Articles