COVID-19 and Pneumonia Detection using Deep Weighted Ensemble Model

The COVID-19 pandemic has brought us face to face with an unprecedented global crisis. However, it has also made us realize that problems like these can only be triumphed by the collectivization of human efforts and resources across the globe. Efforts towards achieving a higher accuracy in detection and classification of COVID-19 are of seminal importance, and this work is intended to contribute in that direction. The recent advances in Deep Learning, and its success, have proven to provide best and accurate solutions to medical imaging. In this study, we propose a Deep Ensemble model (Weighted Ensemble Model), in which we combine the output of five different state-of-art Deep Convolutional Neural Networks (DCNNs) wherein the output of each model is given a weight proportional to its individual accuracy on the given dataset. Weighted sum of the output of ensemble model is used to determine the class of input X-Ray image {COVID-19, Pneumonia, Normal}. Our approach of aggregating the outputs of various models helped us in achieving 100% accuracy, 100% precision and 100% recall as well.