Improving RepVGG model with variational data imputation in COVID-19 classification

Abstract

Millions of fatal cases have been reported worldwide as a result of the Coronavirus disease 2019 (COVID-19) outbreak. In order to stop the spreading of disease, early diagnosis and quarantine of infected people are one of the most essential steps. Therefore, due to the similar symptoms of SARS-CoV-2 virus and other pneumonia, identifying COVID-19 still exists some challenges. Reverse transcription-polymerase chain reaction (RT-PCR) is known as a standard method for the COVID-19 diagnosis process. Due to the shortage of RT-PCR toolkit in global, Chest X-Ray (CXR) image is introduced as an initial step to support patient’s classification. Applying deep learning in medical imaging becomes an advanced research trend in many applications. In this research, RepVGG pre-trained model is considered to be used as the main backbone of the network. Besides, variational autoencoder (VAE) is firstly trained to perform lung segmentation. Afterwards, the encoder part in VAE is preserved as an additional feature extractor to combine with RepVGG performing classification. A COVID-19 radiography database consisting of 3 classes COVID-19, Normal and Viral Pneumonia is conducted. The obtained average accuracy of the proposed model is 95.4% and other evaluation metrics also show better results compared with the original RepVGG model.