Covid-EnsembleNet: An Ensemble Based Approach for Detecting Covid-19 by utilising Chest X-ray Images
Abdullah Al-Monsur, Md Rizwanul Kabir, Abrar Mohammad Ar-Rafi, Mirza Muntasir Nishat, Fahim Faisal
Abstract
Covid-19 is still running rampant around the globe. With the recent emergence of rapidly spreading variants, the necessity for testing becomes ever more acute. In this study, firstly, a deep learning based framework is proposed to conduct both a binary and multi-class classification of chest X-ray images to detect Covid-19 in order to meet the demands of swift, accurate testing worldwide. It is carried out using Convolutional Neural Networks to comprehensively examine the Covid-19 Chest X-ray dataset in conjunction with X-ray images of lungs with pneumonia. The architecture developed for the classification process is termed as CovidNet and its performance is compared with the existing Vgg16 architecture. Secondly, in order to obtain an enhanced performance, the proposed CovidNet is coupled with the Vgg16 architecture by means of ensembling to produce the Covid-EnsembleNet model. In the binary classification process, the developed CovidNet architecture results in a test accuracy of 99.12% while the Vgg16 architecture performs with a 99.34% accuracy. The Covid-EnsembleNet yields an accuracy of 99.56% in this process thereby bolstering the proposed model. Afterwards, in the multi-class classification process the CovidNet achieves a test accuracy of 94.96 % with the Vgg16 achieving a test accuracy of 96.75%. The proposed ensemble model Covid-EnsembleNet yields a test accuracy 97.56 %, thereby, outperforming both the CovidNet and existing Vgg16 architecture in both types of classification.