Automated Diagnosis of COVID-19 Using Deep Learning and Data Augmentation on Chest CT

Abstract

Background Coronavirus disease 2019 has surprised the world since the beginning of 2020, and the rapid growth of COVID-19 is beyond the capability of doctors and hospitals that could deal in many areas. The chest computed tomography (CT) could be served as an effective tool in detection of COVID-19. It is valuable to develop automatic detection of COVID-19. Materials and MethodsThe collected dataset consisted of 1042 chest CT images (including 521 COVID-19, 397 healthy, 76 bacterial pneumonia and 48 SARS) obtained by exhaustively searching available data on the Internet. Then, these data are divided into three sets, referred to training set, validation set and testing set. Sixteen data augmentation operations are designed to enrich the training set in deep learning training phase. Multiple experiments were conducted to analyze the performance of the model in the detection of COVID-19 both in case of no noisy labels and noisy labels. The performance was assessed by the area under the receiver operating characteristic (AUC), sensitivity, specificity and accuracy. Results The data augmentation operations on the training set are effective for improvement of the model performance. The area under the receiver operating characteristic curve is 0.9689 with (95% CI: 0.9308, 1) in case of no noisy labels for the classification of COVID-19 from heathy subject, while the per-exam sensitivity, specificity and accuracy for detecting COVID-19 in the independent testing set are 90.52%, 91.58% and 91.21%, respectively. In the classification of COVID-19 from other hybrid cases, the average AUC of the proposed model is 0.9222 with (95%CI: 0.8418, 1) if there are no noisy labels. The model is also robust when part of the training samples is marked incorrectly. The average AUC is 92.23% in the case of noisy labels of 10% in the training set. Conclusion A deep learning model with insufficient samples can be developed by using data augmentation in assisting medical workers in making quick and correct diagnosis of COVID-19.

Review on Diagnosis of COVID-19 from Chest CT Images Using Artificial Intelligence

“…Hu et al [ 10 ] constructed an AI model on ShuffleNet V2 [ 11 ], which provides fast and accurate training in transfer learning applications. The considered CT dataset consists of 521 COVID-19 infected images, 397 healthy images, 76 bacterial pneumonia images, and 48 SARS images.…”

“…In addition to classify COVID-19 and normal cases, Hu et al [ 10 ] performed another experiment to differentiate COVID-19 cases from other cases as bacterial pneumonia and SARS. The average sensitivity, specificity, and the AUC score were obtained as 0.8571, 84.88%, and 92.22%, respectively.…”

Applications of artificial intelligence in battling against covid-19: A literature review

“…Other examples of deep neural networks in processing CT scan images of chest include [200] , [201] , [202] , [203] , [204] , [205] , [206] , [207] , [208] , [209] , [210] , [211] , [212] , [213] , [214] , [215] , [216] , [217] , [218] , [219] , [220] , [221] , [222] , [223] , [224] , [225] , [226] , [227] , [228] , [229] , [230] , [231] , [232] , [233] , [234] , [235] .…”

Diagnostic tools and automated decision support systems for COVID-19

“…Hu et al. [75] developed a light-weight deep learning model to distinguish COVID-19 from healthy cases followed by classification of images into COVID-19 and other types of pneumonia. Multiple data augmentation techniques were tested to enhance the training set used in deep-learning modeling.…”

“…As for ‘‘AI-assisted diagnostic” using CT, the diagnostic precision of the model is often calculated as the ability to distinguish between COVID-19 and other pneumonia cases, to quantitatively assess the disease severity and progression, and to segment the COVID-19 infected regions inside the lung. The deep-learning powered AI systems using CT images are reported to achieve COVID-19 identification accuracy at the level of >90% in the disease classification task, AUC score of >96% in discriminating between severe and nonsevere stages, and an average Dice coefficient of 75%, while comparing the lung segmentation performance against expert annotations [74] , [75] , [76] , [78] , [79] .…”