Detecting Covid-19 and Other Lung Diseases with Deep Learning

Since December 2019, Covid-19, also known as severe acute respiratory syndrome (SARSCOV-2), has infected about 531 million individuals worldwide causing devastating consequences. Scientists, health professionals, and radiologists have all invested huge amounts of time, effort and resources in developing faster and accurate methods for detecting the virus as it spreads all over the world. The current gold standards for detecting covid-19 such as RT-PCR, POC, Droplet-based digital PCR (dPCR) and Immunoassays are not only expensive for low-income countries but also require substantial human expert knowledge which in turn makes them time-consuming and susceptible to manipulation. The study presented in this paper aims to address these issues by proposing a model to automatically detect COVID-19 in chest X-rays using Deep Learning techniques. Specifically, the model uses convolutional neural networks (CNNs), a deep learning technique, which has been shown to produced very good results when applied to medical image diagnosis problems. Our proposed model will therefore not only provide a cheaper, faster and accurate method of detecting COVID-19 but will also provide an easy to use web and mobile platform for non-experts. While current detection techniques such the RT-PCR are still considered as the most effective, our CNN-based model was also able to produce good results when applied to the COVID-19 radiography dataset. For instance, the model was able to detect COVID-19 from X-ray images with an accuracy rate of 90 percent. Furthermore, the model was also able to automatically detect other respiratory diseases such as viral pneumonia and lung opacity with an accuracy rate of over 90 percent. Given the potential of the automated detection of COVID-19 established in this study, future work will extend this work by incorporating automated search techniques such as genetic algorithms in the fine tuning of the model’s parameters in order to obtain an even higher accuracy rate.