Detection of Lung Lesions in Chest X-ray Images based on Artificial Intelligence

Chuan Yi Wei, Chih Ying Ou, I. Yen Chen, Hsuan Ting Chang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Tuberculosis (TB) remains the most common cause of death from a single infectious agent. Early detection and treatment can limit the spread of the disease. One of the critical needs is to use existing diagnostic techniques effectively. Chest X-rays (CXR) examination is the primary diagnostic tool for tuberculosis. In this paper, we propose a deep learning framework for multiclass TB lesion semantic segmentation. Image augmentation and contrast limited adaptive histogram equalization (CLAHE) are used to improve the accuracy of segmentation results. We compare the performance of U-Net and U-Net++ networks. The experimental results show that we could achieve 100% image classification accuracy with U-Net++. On the other hand, the mean intersection over union (Mean IoU) of the detected multiclass lesions can achieve as high as 0.7. The proposed method can speed up TB diagnosis in low and middle-income countries where there is a lack of medical expertise and a severe TB epidemic.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages173-174
Number of pages2
ISBN (Electronic)9781665470506
DOIs
Publication statusPublished - 2022
Event2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 - Taipei, Taiwan
Duration: 2022 Jul 62022 Jul 8

Publication series

NameProceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Conference

Conference2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Country/TerritoryTaiwan
CityTaipei
Period22-07-0622-07-08

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering
  • Media Technology
  • Health Informatics
  • Instrumentation

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