<scp>LiteCovidNet</scp>: A lightweight deep neural network model for detection of <scp>COVID</scp>‐19 using X‐ray images
Sachin Kumar, Sourabh Shastri, Shilpa Mahajan, Kuljeet Singh, Surbhi Gupta, Rajneesh Rani, Neeraj Mohan, Vibhakar Mansotra
Abstract
The syndrome called COVID-19 which was firstly spread in Wuhan, China has already been declared a globally "Pandemic." To stymie the further spread of the virus at an early stage, detection needs to be done. Artificial Intelligence-based deep learning models have gained much popularity in the detection of many diseases within the confines of biomedical sciences. In this paper, a deep neural network-based "LiteCovidNet" model is proposed that detects COVID-19 cases as the binary class (COVID-19, Normal) and the multi-class (COVID-19, Normal, Pneumonia) bifurcated based on chest X-ray images of the infected persons. An accuracy of 100% and 98.82% is achieved for binary and multi-class classification respectively which is competitive performance as compared to the other recent related studies. Hence, our methodology can be used by health professionals to validate the detection of COVID-19 infected patients at an early stage with convenient cost and better accuracy.