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

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. © 2021 IEEE.

Classification of Pulmonary Viruses X-ray and Detection of COVID-19 Based on Invariant of Inception-V 3 Deep Learning Model

Coronavirus (COVID-19) is a catastrophic illness that has already infected several million individuals and caused thousands of fatalities globally. Any technical technique that enables quick testing of the COVID-19 with high accuracy might be essential for healthcare providers. X-ray imaging is an easily available technique that might be a great option for its quick detection. This research was conducted to examine the usefulness of artificial intelligence (AI) to detect COVID-19 quickly and accurately from chest X-ray scans. The objective of this study is to provide a solid technical method for the automatic identification of COVID-19, Pneumonia, Lung opacity, and Normal digital X-ray scans using pretrained, deep learning algorithms while optimizing detection accuracy. Inception v3 with an additional added dense layer is used with image augmentation to train and validate the selected dataset. The obtained accuracy of 99.72% promises speedy detection of COVID-19. © 2021 IEEE.