COVID-19 Detection from Chest X-Rays

Abstract

The outbreak of COVID-19 in different parts of the world is a major concern for all the administrative units of respective countries. COVID-19 has infected 223 countries and caused 45.5 lakh deaths worldwide. India is also facing this very tough task for controlling the virus outbreak and has managed its growth rate through some strict measures. Early diagnosis of COVID patients is a critical challenge for medical practitioners, governments, organizations, and countries to overcome the rapid spread of the deadly virus in any geographical area.AI plays an essential role in COVID-19 case classification as we can apply machine learning models on COVID-19 case data to predict infectious cases and recovery rates using chest x-ray. Given recent developments in the application of machine learning models to medical imaging problems, there is fantastic promise for applying machine learning methods to COVID-19 radiological imaging for improving the accuracy of diagnosis, compared with the gold-standard RT–PCR, while also providing valuable insight for prognostication of patient outcomes. This study is aimed at evaluating the effectiveness of the state-of-the-art pre trained Convolutional Neural Networks (CNNs) on the automatic diagnosis of COVID-19 from chest X-rays (CXRs). The dataset used in the experiments consists of 1200 CXR images from individuals with COVID-19, 1345 CXR images from individuals with viral pneumonia, and 1341 CXR images from healthy individuals. In this paper, the effectiveness of artificial intelligence (AI) in the rapid and precise identification of COVID-19 from CXR images has been explored based on different pre trained deep learning algorithms and fine-tuned to maximize detection accuracy to identify the best algorithms.