Artificial Intelligence: A Key for Detecting COVID‐19 Using Chest Radiography

Abstract

An impending branch of computer science is artificial intelligence. It plays an important role in the construction of smart machines that are capable of performing sophisticated operations. One of the key characteristics of artificial intelligence is its ability to make decisions on its own and rationalize the solution, helping us to achieve a certain goal. Our human race has faced many threats in the form of epidemics and pandemics, which have proved to be almost incurable in the past. Nevertheless, science and its evolving technologies have given us some hope to fight such threats. One such pandemic that our human race is facing in the current times is COVID-19. This deadly disease is rapidly spreading across the whole world endangering the lives of humans. Amid the chaos, we desperately need to stop the spread, or at least take adequate counter-active measures to detect this virus at its early stage. Deep learning, a subset of artificial intelligence provides many models which helps in the automation of the task of detecting viruses in humans mainly with the help of image processing. In detecting COVID-19, deep learning is a breakthrough, which has helped us in our proposed system. This system makes use of chest radiographs (CXR) to detect the presence of the virus in the human body thereby lowering the risk of spread which is fairly high in manual detection methods. The CXRs are one of the most common imaging tests in the clinical field, which helps in detecting the presence of cold, cough, shortness of breath in the lungs, and so on. The proposed model is very efficient when it comes to detecting problems in the lungs with the help of image processing. We propose an improvised neural network derived from the Convolutional Neural Network which works similar to the human brain structure to detect and process the CXR images efficiently and at faster rates. The neural network mimics the functioning of the brain, where self-learning and decision making are its key features. The image data sets are a collection of CXR images which have a RGB value of 1. This approach is proven to be safer and better than the manual testing methods that are currently deployed. As the traditional methods for detecting COVID-19 virus is tedious, and not fairly accurate, automating this task can help in giving accurate results with reduced risk of spread of disease through physical contact.

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