An Automatic Detection Method for COVID-19 in CT Images. Issue 1 (1st May 2022)

• Record Type: Journal Article
• Authors: Li, Yarong; Jiang, Yizhang; Gu, Yi; Qian, Pengjiang
• Is Part Of: Journal of physics. Volume 2278:Issue 1(2022)
• Publication Date: 2022-05-01
• Subjects: Physics -- Congresses; 530.5
• DOI: 10.1088/1742-6596/2278/1/012044
• Languages: English
• ISSNs: 1742-6588

Abstract

Abstract: There are many new cases of new coronary pneumonia (named COVID-19) every day around the world. In such a severe situation, effective detection of COVID-19 has become extremely important. Studies have shown that chest CT images can be used for COVID-19 detection because they can show bilateral changes in the lungs of people infected with COVID-19. It is not difficult for experienced radiologists to make preliminary judgments based on CT images. However, with the emergence of a large number of suspected cases, the explosive demand has overwhelmed doctors. Therefore, the automatic diagnosis of COVID-19 CT images is of great significance to realize early diagnosis, early isolation, and early treatment. In this paper, we study an automatic identification method that uses transfer learning technology, which transfers the pre-trained VGG16 network for feature extraction, and combines VAE augmentation data to reduce over-fitting, and finally uses integrated technology to achieve better detection results. Our experiments have shown that the accuracy of our method for identifying whether a patient's CT image is positive or negative for COVID-19 is 91%, the precision is 88%, the recall rate is 94%, and the F1-score is 91%. Compared with other the state of art methods, the method proposed in this article can provide more efficient classification and identification of COVID-19.