Diagnosis of COVID-19 from Chest X-rays Using CNN and Determination of Its Severity by Text Analysis

ABSTRACT

In India, the effect of COVID-19 has been worst because of various reasons like huge population, lack of necessary medical infrastructure, lack of awareness among people, inability to identify people with actual severe conditions and many more. Some people are waiting for more than a day to get the test results besides having rapid diagnosing kits. Due to a lack of awareness among people, patients with mild conditions are joining hospitals, leaving no place for severely infected patients. There is a need to automate the diagnosis of COVID-19 and identify the people with actual severe conditions so that those patients can be equipped with the required medical infrastructure and can potentially stop the process of spreading the disease and can even reduce the mortality rate. This need motivated us to propose a model which can diagnose COVID-19 and detect patients with severe conditions. Chest X-rays of individuals are efficient and can be used for rapid diagnosis of COVID-19 as X-ray centers are available even at rural areas. The proposed system automates the detection of COVID-19 and distinguishes the COVID-19 cases from other pneumonia and normal cases using a 11-layer Convolution Neural Network (CNN) model. We can use text analysis techniques on the patient's health condition which can be obtained by collecting details of the patient like age, body temperature, need for supplementary oxygen requirement, etc., we can identify the severity of the disease. The proposed CNN model achieved a 0.84 accuracy and on test data. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.