A deep learning model for mass screening of COVID-19

The objective of this research is to develop a convolutional neural network model 'COVID-Screen-Net' for multi-class classification of chest X-ray images into three classes viz. COVID-19, bacterial pneumonia, and normal. The model performs the automatic feature extraction from X-ray images and accurately identifies the features responsible for distinguishing the X-ray images of different classes. It plots these features on the GradCam. The authors optimized the number of convolution and activation layers according to the size of the dataset. They also fine-tuned the hyperparameters to minimize the computation time and to enhance the efficiency of the model. The performance of the model has been evaluated on the anonymous chest X-ray images collected from hospitals and the dataset available on the web. The model attains an average accuracy of 97.71% and a maximum recall of 100%. The comparative analysis shows that the 'COVID-Screen-Net' outperforms the existing systems for screening of COVID-19. The effectiveness of the model is validated by the radiology experts on the real-time dataset. Therefore, it may prove a useful tool for quick and low-cost mass screening of patients of COVID-19. This tool may reduce the burden on health experts in the present situation of the Global Pandemic. The copyright of this tool is registered in the names of authors under the laws of Intellectual Property Rights in India with the registration number 'SW-13625/2020'.