Deep learning-based automated multiclass classification of chest X-rays into Covid-19, normal, bacterial pneumonia and viral pneumonia

Abstract

Covid-19 has been a pandemic across almost all parts of the world. Due to its higher spread rate and increased mortality rate, early detection of this is required. In the present study, we have used chest X-Rays to identify the presence of Covid-19 and several other Pneumonia types (Viral and Bacterial). To perform this classification, we have used a transfer learning-based model relying upon a pre-trained VGG-16 classifier network. Along with that, we have used the inception module as a pre-processing cursor to this network. We present our model via three case study approaches, namely – Case (01) – four-class classification, Case (02) – three-class classification, and Case (03) – two-class classification. For these case studies, we have selected our classes from Normal, Covid-19, Viral Pneumonia, and Bacterial Pneumonia. We have evaluated our model’s classification performance on various parameters, such as—accuracy, precision, sensitivity, specificity, false-positive rate, and F1-score, as just one parameter is not sufficient enough to evaluate the performance. After training the network for all three cases, we have found Covid-19 classification accuracies – Case 01–91.86% (Four Classes), Case 02–97.67% (Three Classes), and Case 03–99.61% (Two Classes) and all the other parameters are well represented in the performance parameter section. Our proposed model testing accuracies for all three cases are – Case 01–87.32% (Four Classes), Case 02–96.89% (Three Classes), and Case 03–99.95% (Two Classes). Based on the achieved accuracies, our model showed comparable performance to pre-existing methods like VGG-16, Res-Net, and Inception-Net. We can use these case studies for the interpretation and classification of chest X-Rays in these classes and with increased dataset and computational power, we can apply the proposed model for more class classification.