Efficient-CovidNet: Deep learning based COVID-19 detection from chest X-ray images

ABSTRACT

The COVID-19 pandemic has wreaked havoc all over the world. The rising number of cases have overburdened healthcare systems even in the most developed countries. To ease the burden on healthcare systems a quick and efficient testing technique is needed. Currently, the RT-PCR testing is done with time consuming and laborious an alternative is a detection from Chest X-Ray images. It has been discovered in published studies that Chest X-Rays of COVID-19 patients have specific malformations that can be used to identify a positive case. Inspired by the work done on 'COVID-Net' by Linda Wang, Zhong Qiu Lin and Alexander Wong, a Deep Learning approach to detect coronavirus from Chest X-Ray images is used in this study. To surpass previous results the EfficientNet Convolutional Neural Network (CNN) model is proposed. This model not only achieves +2% accuracy, but it also attains higher sensitivity and Positive Predictive Values. The study uses the open source COVIDx dataset. It has approximately 14, 000 X-Ray images. To the best of authors' knowledge, this dataset contains the largest number of COVID-19 positive cases. The study offers a Deep Learning approach contributing to create an efficient COVID-19 detector that can be used in the real world. © 2021 IEEE.