Publication: COVID-19 detection and heatmap generation in chest x-ray images
Issued Date
2021-01-01
Resource Type
ISSN
23294310
23294302
23294302
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2-s2.0-85133809088
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Medical Imaging. Vol.8, No.S1 (2021)
Suggested Citation
Worapan Kusakunniran, Sarattha Karnjanapreechakorn, Thanongchai Siriapisith, Punyanuch Borwarnginn, Krittanat Sutassananon, Trongtum Tongdee, Pairash Saiviroonporn COVID-19 detection and heatmap generation in chest x-ray images. Journal of Medical Imaging. Vol.8, No.S1 (2021). doi:10.1117/1.JMI.8.S1.014001 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/78511
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Title
COVID-19 detection and heatmap generation in chest x-ray images
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Abstract
Purpose: The outbreak of COVID-19 or coronavirus was first reported in 2019. It has widely and rapidly spread around the world. The detection of COVID-19 cases is one of the important factors to stop the epidemic, because the infected individuals must be quarantined. One reliable way to detect COVID-19 cases is using chest x-ray images, where signals of the infection are located in lung areas. We propose a solution to automatically classify COVID-19 cases in chest x-ray images. Approach: The ResNet-101 architecture is adopted as the main network with more than 44 millions parameters. The whole net is trained using the large size of 1500 × 1500 x-ray images. The heatmap under the region of interest of segmented lung is constructed to visualize and emphasize signals of COVID-19 in each input x-ray image. Lungs are segmented using the pretrained U-Net. The confidence score of being COVID-19 is also calculated for each classification result. Results: The proposed solution is evaluated based on COVID-19 and normal cases. It is also tested on unseen classes to validate a regularization of the constructed model. They include other normal cases where chest x-ray images are normal without any disease but with some small remarks, and other abnormal cases where chest x-ray images are abnormal with some other diseases containing remarks similar to COVID-19. The proposed method can achieve the sensitivity, specificity, and accuracy of 97%, 98%, and 98%, respectively. Conclusions: It can be concluded that the proposed solution can detect COVID-19 in a chest x-ray image. The heatmap and confidence score of the detection are also demonstrated, such that users or human experts can use them for a final diagnosis in practical usages.