Abstract
The newly identified Coronavirus pneumonia, subsequently termed COVID-19, is highly transmittable and pathogenic with no clinically approved antiviral drug or vaccine available for treatment. The most common symptoms of COVID-19 are dry cough, sore throat, and fever. Symptoms can progress to a severe form of pneumonia with critical complications, including septic shock, pulmonary edema, acute respiratory distress syndrome and multi-organ failure. While medical imaging is not currently recommended in Canada for primary diagnosis of COVID-19, computer-aided diagnosis systems could assist in the early detection of COVID-19 abnormalities and help to monitor the progression of the disease, potentially reduce mortality rates. In this study, we compare popular deep learning-based feature extraction frameworks for automatic COVID-19 classification. To obtain the most accurate feature, which is an essential component of learning, MobileNet, DenseNet, Xception, ResNet, InceptionV3, InceptionResNetV2, VGGNet, NASNet were chosen amongst a pool of deep convolutional neural networks. The extracted features were then fed into several machine learning classifiers to classify subjects as either a case of COVID-19 or a control. This approach avoided task-specific data pre-processing methods to support a better generalization ability for unseen data. The performance of the proposed method was validated on a publicly available COVID-19 dataset of chest X-ray and CT images. The DenseNet121 feature extractor with Bagging tree classifier achieved the best performance with 99% classification accuracy. The second-best learner was a hybrid of the a ResNet50 feature extractor trained by LightGBM with an accuracy of 98
【저자키워드】 coronavirus disease, deep learning, Feature extraction, transfer learning, computer-aided diagnosis, Lung Opacity, 【초록키워드】 COVID-19, Treatment, Vaccine, Pneumonia, Diagnosis, progression, antiviral drug, Accuracy, Fever, Septic shock, complications, Pulmonary edema, chest X-ray, dataset, Canada, Critical, acute respiratory distress, Support, sore throat, Classifier, subject, dry cough, syndrome, help, multi-organ failure, pathogenic, symptoms of COVID-19, abnormality, while, MONITOR, approach, feature, reduce mortality, clinically, the disease, approved, assist, diagnosis of COVID-19, machine learning classifier, 【제목키워드】 COVID-19, machine learning, detection, X-ray, Automatic,