ResidualCovid-Net: An Interpretable Deep Network to Screen COVID-19 Utilizing Chest CT Images

Abstract

Coronavirus illness, commonly abbreviated as COVID-19, has been designated a global pandemic. To prevent the spread of this deadly virus, those who are infected must be quarantined or evacuated. In this situation, a quick and systematic testing toolkit is required. Recent research has discovered that radiography chest CT has significant patterns and attributes that may be utilized to precisely identify COVID-19. A deep learning-based network called ResidualCovid-Net was suggested in this study to identify COVID-19 infestations using CT scans. The proposed ResidualCovid-Net is inspired by the original Resnet architecture. Another barrier in this aspect is clinically distinguishing among COVID-19, pneumonia and normal instances. ResidualCovid-Net was designed to identify anomalies in CT scans that may successfully delineate COVID-19, common pneumonia and normal cases. Gradients weighted class activation maps showed how well the network located anomalies in CT images and demonstrated the network’s generalization ability.