Convolutional neural network for Covid-19 detection from X-ray images

Jain, A.; Ratnoo, S.; Kumar, D.

Covid-19 pandemic has crumbled the health systems of the nation's world over. In such a scenario, quick and accurate detection of coronavirus infection plays an important role in timely referral of physicians and control transmission of the disease. RT-PCR is the most widely used method for identification of coronavirus disease 19 patients, but it is time consuming and takes two to three days to deliver the report. Researchers around the world are looking for alternative machine learning techniques including deep learning to assist the medical experts for early Covid-19 disease diagnosis from medical pictures such as X-ray films and CT scans. Since the facility for chest X-rays is available even in smaller towns and is relatively less expensive, it would be useful to design machine learning methods for proving initial Covid-19 detection from chest X-rays to contain this pandemic. Thus, in this work, we propose a Convolutional Neural Network (CNN or ConvNet) for the finding of presence and absence of Covid-19 disease. We compare the CNN model with traditional and transfer learning-based machine learning algorithms. The proposed CNN is accurate compared to the traditional machine learning algorithms (KNN, SVM, DT etc.). The suggested CNN model is almost as accurate as the classifiers based on transfer learning (such as InceptionV3, VGG16 and ResNet50) despite being simple in terms of number of parameters learnt. The CNN model takes less training time. © 2021 IEEE.