A Novel Deep Learning Algorithm for Covid Detection and Classification

Abstract

The prediction of future development of a natural phenomenon is one of the main objectives of recent technology, but this is a great challenge when dealing with an epidemic or pandemic. This proved to be particularly true in the case of Covid-19 global pandemic that the world is suffering and facing since January 2020. The response to the virus infection are partially known, however the immune system is mostly affected especially in patients with pre-existing respiratory or systemic diseases. Most infections by coronavirus are mild and self-treated. Therefore, in early stages of the disease, it will be misleading to estimate the real spread of the virus based on the reports of hospital. Moreover, such reports vary according to how measurements are performed, and the number of tests related only to the number of symptomatic patients. Despite all this, the large amount of official data published in last months, and updated daily has motivated various mathematical models, which are required to predict the evolution of an epidemic and plan effective control strategies. Due to the incompleteness of the data and intrinsic complexity, predicting the evolution, the peak or the end of the pandemic is a challenge. In this paper, a deep learning based approach is proposed aiming to evaluate a-priori risk of an epidemic caused by Covid-19. The proposed algorithm leverages image processing and deep learning algorithms to detect Covid and differentiate between normal, Covid affected, lung opacity and viral pneumonia affected chest x-rays. This results in setting strategies to prevent or decrease the impact of future epidemic waves. The accuracy for the proposed algorithm is 95.01% and Recall is 98.5% on validation data. The inference is that combining image processing with deep learning can improve performance of Covid detection.