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

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 fo...

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Bibliographic Details
Main Authors: Wei, Chuan-Yi, Ou, Chih-Ying, Chen, I-Yen, Chang, Hsuan-Ting
Format: Conference Proceeding
Language:English
Subjects:
Artificial intelligence
Chest X-rays
deep learning
Histograms
Image segmentation
Lesions
Lung
semantic segmentation
Semantics
Training
Tuberculosis
U-net
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Summary: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.
ISSN:2575-8284
DOI:10.1109/ICCE-Taiwan55306.2022.9869143