Deep convolution neural networks to differentiate between <scp>COVID</scp> ‐19 and other pulmonary abnormalities on chest radiographs: Evaluation using internal and external datasets

We aimed to evaluate the performance of convolutional neural networks (CNNs) in the classification of coronavirus disease 2019 (COVID‐19) disease using normal, pneumonia, and COVID‐19 chest radiographs (CXRs). First, we collected 9194 CXRs from open datasets and 58 from the Korea University Anam Hospital (KUAH). The number of normal, pneumonia, and COVID‐19 CXRs were 4580, 3884, and 730, respectively. The CXRs obtained from the open dataset were randomly assigned to the training, tuning, and test sets in a 70:10:20 ratio. For external validation, the KUAH (20 normal, 20 pneumonia, and 18 COVID‐19) dataset, verified by radiologists using computed tomography, was used. Subsequently, transfer learning was conducted using DenseNet169, InceptionResNetV2, and Xception to identify COVID‐19 using open datasets (internal) and the KUAH dataset (external) with histogram matching. Gradient‐weighted class activation mapping was used for the visualization of abnormal patterns in CXRs. The average AUC and accuracy of the multiscale and mixed‐COVID‐19Net using three CNNs over five folds were (0.99 &#177; 0.01 and 92.94% &#177; 0.45%), (0.99 &#177; 0.01 and 93.12% &#177; 0.23%), and (0.99 &#177; 0.01 and 93.57% &#177; 0.29%), respectively, using the open datasets (internal). Furthermore, these values were (0.75 and 74.14%), (0.72 and 68.97%), and (0.77 and 68.97%), respectively, for the best model among the fivefold cross‐validation with the KUAH dataset (external) using domain adaptation. The various state‐of‐the‐art models trained on open datasets show satisfactory performance for clinical interpretation. Furthermore, the domain adaptation for external datasets was found to be important for detecting COVID‐19 as well as other diseases.