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

研究成果: Conference contribution

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
主出版物標題Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
發行者Institute of Electrical and Electronics Engineers Inc.
頁面173-174
頁數2
ISBN(電子)9781665470506
DOIs
出版狀態Published - 2022
事件2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 - Taipei, Taiwan
持續時間: 2022 7月 62022 7月 8

出版系列

名字Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Conference

Conference2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
國家/地區Taiwan
城市Taipei
期間22-07-0622-07-08

All Science Journal Classification (ASJC) codes

  • 人工智慧
  • 電腦科學應用
  • 硬體和架構
  • 可再生能源、永續發展與環境
  • 電氣與電子工程
  • 媒體技術
  • 健康資訊學
  • 儀器

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