Journal
INTERDISCIPLINARY SCIENCES-COMPUTATIONAL LIFE SCIENCES
Volume 13, Issue 2, Pages 273-285Publisher
SPRINGER HEIDELBERG
DOI: 10.1007/s12539-021-00420-z
Keywords
Diagnosis of COVID-19; Pneumonia classifying; CT image; Deep learning network
Categories
Funding
- National Key R&D Program of China [2018YFC1315405]
- National Natural Science Foundation of China [U1611261, 61772566, 81801132, 81871332]
- Guangdong Frontier and Key Tech Innovation Program [2018B010109006, 2019B020228001]
- Natural Science Foundation of Guangdong, China [2019A1515012207]
- Introducing Innovative and Entrepreneurial Teams [2016ZT06D211]
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Computed tomography (CT) is an efficient method for diagnosing COVID-19, but developing an automatic CT image diagnosis system is necessary for assisting doctors due to the time and concentration required in reading CT films. This study focused on distinguishing COVID-19 from typical viral pneumonia using a newly developed model combining ResNet50 and SE blocks, achieving high accuracy and performance in classification.
Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films brings a lot of concentration and time for doctors. Therefore, it is necessary to develop an automatic CT image diagnosis system to assist doctors in diagnosis. Previous studies devoted to COVID-19 in the past months focused mostly on discriminating COVID-19 infected patients from healthy persons and/or bacterial pneumonia patients, and have ignored typical viral pneumonia since it is hard to collect samples for viral pneumonia that is less frequent in adults. In addition, it is much more challenging to discriminate COVID-19 from typical viral pneumonia as COVID-19 is also a kind of virus. In this study, we have collected CT images of 262, 100, 219, and 78 persons for COVID-19, bacterial pneumonia, typical viral pneumonia, and healthy controls, respectively. To the best of our knowledge, this was the first study of quaternary classification to include also typical viral pneumonia. To effectively capture the subtle differences in CT images, we have constructed a new model by combining the ResNet50 backbone with SE blocks that was recently developed for fine image analysis. Our model was shown to outperform commonly used baseline models, achieving an overall accuracy of 0.94 with AUC of 0.96, recall of 0.94, precision of 0.95, and F1-score of 0.94. The model is available in https://github.com/Zhengfudan/COVID-19-Diagnosis-and-Pneumonia-Classification.
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