Accurate Prediction of COVID-19 using Chest X-Ray Images through Deep Feature Learning model with SMOTE and Machine Learning Classifiers

Rahul Kumar; Ridhi Arora; Vipul Bansal; Vinodh J Sahayasheela; Himanshu Buckchash; Javed Imran; Narayanan Narayanan; Ganesh N Pandian; Balasubramanian Raman

Fast COVID-19 recognition can help control disease transmission and will help to monitor the progression of infectious disease. According to Tao et al. 18 , it seems that Chest CT is more prone to COVID-19 diagnosis with respect to the original reverse-transcription polymerase chain reaction (RT-PCR) that has been collected from swab samples from patients and reported an accuracy of 97.3% to classify COVID-19 viral highly infectious diseases. Convolution neural network (CNN) is one of the most popular algorithms that have shown high precision in the ability to interpret the COVID-19 classification with medical images like X-rays or CT images. Wang et al. 19 has proposed a COVID-19 classification technique by implementing the CNNs based on Inception Net over the pathogen-confirmed COVID-19, with 453 computed tomography (CT) images and reported an accuracy of 82.9%. Songet al 20 has implemented a multi-class classification to recognise the diseases (COVID-19 viral infection, non-COVID and bacterial pneumonia) with CT images (88 patients with COVID-19 infected, 86 non-COVID patients and 100 patients with bacterial pneumonia)by using a modified version of pre-trained ResNet-50 net-work, named as DRE-Net and reported an accuracy of 86% for bacterial pneumonia and viral pneumonia (COVID- 19) classification.