TLCoV- An automated Covid-19 screening model using Transfer Learning from Chest X-ray images

Das, Ayan Kumar; Kalam, Sidra; Kumar, Chiranjeev; Sinha, Ditipriya

The Coronavirus disease (Covid-19) has been declared a pandemic by World Health Organisation (WHO) and till date caused 585,727 numbers of deaths all over the world. The only way to minimize the number of death is to quarantine the patients tested Corona positive. The quick spread of this disease can be reduced by automatic screening to cover the lack of radiologists. Though the researchers already have done extremely well to design pioneering deep learning models for the screening of Covid-19, most of them results in low accuracy rate. In addition, over-fitting problem increases difficulties for those models to learn on existing Covid-19 datasets. In this paper, an automated Covid-19 screening model is designed to identify the patients suffering from this disease by using their chest X-ray images. The model classifies the images in three categories – Covid-19 positive, other pneumonia infection and no infection. Three learning schemes such as CNN, VGG-16 and ResNet-50 are separately used to learn the model. A standard Covid-19 radiography dataset from the repository of Kaggle is used to get the chest X-ray images. The performance of the model with all the three learning schemes has been evaluated and it shows VGG-16 performed better as compared to CNN and ResNet-50. The model with VGG-16 gives the accuracy of 97.67%, precision of 96.65%, recall of 96.54% and F1 score of 96.59%. The performance evaluation also shows that our model outperforms two existing models to screen the Covid-19.