RESUMEN
With over 172 Million people infected with the novel coronavirus (COVID-19) globally and with the numbers increasing exponentially, the dire need of a fast diagnostic system keeps on surging. With shortage of kits, and deadly underlying disease due to its vastly mutating and contagious properties, the tired physicians need a fast diagnostic method to cater the requirements of the soaring number of infected patients. Laboratory testing has turned out to be an arduous, cost-ineffective and requiring a well-equipped laboratory for analysis. This paper proposes a convolutional neural network (CNN) based model for analysis/detection of COVID-19, dubbed as CovCNN, which uses the patient's chest X-ray images for the diagnosis of COVID-19 with an aim to assist the medical practitioners to expedite the diagnostic process amongst high workload conditions. In the proposed CovCNN model, a novel deep-CNN based architecture has been incorporated with multiple folds of CNN. These models utilize depth wise convolution with varying dilation rates for efficiently extracting diversified features from chest X-rays. 657 chest X-rays of which 219 were X-ray images of patients infected from COVID-19 and the remaining were the images of non-COVID-19 (i.e. normal or COVID-19 negative) patients. Further, performance evaluation on the dataset using different pre-trained models has been analyzed based on the loss and accuracy curve. The experimental results show that the highest classification accuracy (98.4%) is achieved using the proposed CovCNN model.