Multi-Scale Residual Network for Covid-19 Diagnosis Using Ct-Scans

To mitigate the outbreak of highly contagious COVID-19, we need a sensitive, robust automated diagnostic tool. This paper proposes a three-level approach to separate the cases of COVID-19, pneumonia from normal patients using chest CT scans. At the first level, we fine tune a multi-scale ResNet50 model for feature extraction from all the slices of CT scan for each patient. By using multi-scale residual network, we can learn different sizes of infection, thereby making the detection possible at early stages too. These extracted features are used to train a patient-level classifier, at the second level. Four different classifiers are trained at this stage. Finally, predictions of patient level classifiers are combined by training an ensemble classifier. We test the proposed method on three sets of data released by ICASSP, COVID-19 Signal Processing Grand Challenge (SPGC). The proposed method has been successful in classifying the three classes with a validation accuracy of 94.9% and testing accuracy of 88.89%.