COVID-19 Detection Using Convolutional Neural Networks and InceptionV3

Abstract

The world today faces the grave crisis of a pandemic that threatens life, health, industries, economies and society as we know it. To tackle the spread of the COVID-19 disease, it is imperative that the individuals affected by it are identified efficiently. The medical workforce is overwhelmed in many countries, and doctors and nurses must be assisted with rapid automated systems that detect COVID-19 confidently without the need for trained personnel or special manufacturing processes. The currently available testing mechanisms are accurate but take up to 5 days to show results. Automated radiological testing is faster and does not require any testing kits. This study aims to simplify, speed up and validate the process of COVID-19 detection and differentiation from other lung diseases using Artificial Intelligence and Machine Learning principles to implement a cost effective and accurate COVID-19 test by detection of inflamed parts of lungs in chest X-Rays. Two approaches for separate target users are built. In the first approach, a customized Convolutional Neural Network model is built to classify chest X-ray images into two classes- 0(COVID-19 negative) and 1 (COVID-19 positive). This model reached an accuracy of 98%. The Inception v3 architecture (GoogLeNet) was trained for the second application, in which people infected by COVID-19 were distinguished from patients of other underlying lung diseases such as Atelectasis and Pneumonia. This approach demonstrates how reliably an automated COVID-19 test can be extended to people with other lung complications and showed a 91% accuracy.

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