TY - GEN
T1 - Detection of Lung Lesions in Chest X-ray Images based on Artificial Intelligence
AU - Wei, Chuan Yi
AU - Ou, Chih Ying
AU - Chen, I. Yen
AU - Chang, Hsuan Ting
N1 - Funding Information:
This research was funded by the “Intelligent Recognition Industry Service Center” from The Featured Areas Research Center Program within the framework of Higher Education Sprout Project by Ministry of Education (MOE) in Taiwan.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85138736515&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85138736515&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Taiwan55306.2022.9869143
DO - 10.1109/ICCE-Taiwan55306.2022.9869143
M3 - Conference contribution
AN - SCOPUS:85138736515
T3 - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
SP - 173
EP - 174
BT - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
Y2 - 6 July 2022 through 8 July 2022
ER -