Detecting COVID-19 from Chest X-rays Using Convolutional Neural Network Ensembles

Abstract

Starting in late 2019, the coronavirus SARS-CoV-2 began spreading around the world and causing disruption in both daily life and healthcare systems. The disease is estimated to have caused more than 6 million deaths worldwide [WHO]. The pandemic and the global reaction to it severely affected the world economy, causing a significant increase in global inflation rates, unemployment, and the cost of energy commodities. To stop the spread of the virus and dampen its global effect, it is imperative to detect infected patients early on. Convolutional neural networks (CNNs) can effectively diagnose a patient’s chest X-ray (CXR) to assess whether they have been infected. Previous medical image classification studies have shown exceptional accuracies, and the trained algorithms can be shared and deployed using a computer or a mobile device. CNN-based COVID-19 detection can be employed as a supplement to reverse transcription-polymerase chain reaction (RT-PCR). In this research work, 11 ensemble networks consisting of 6 CNN architectures and a classifier layer are evaluated on their ability to differentiate the CXRs of patients with COVID-19 from those of patients that have not been infected. The performance of ensemble models is then compared to the performance of individual CNN architectures. The best ensemble model COVID-19 detection accuracy was achieved using the logistic regression ensemble model, with an accuracy of 96.29%, which is 1.13% higher than the top-performing individual model. The highest F1-score was achieved by the standard vector classifier ensemble model, with a value of 88.6%, which was 2.06% better than the score achieved by the best-performing individual model. This work demonstrates that combining a set of top-performing COVID-19 detection models could lead to better results if the models are integrated together into an ensemble. The model can be deployed in overworked or remote health centers as an accurate and rapid supplement or back-up method for detecting COVID-19.

A Robust Ensemble Convolutional Neural Networks for Diagnosing Chest Diseases

“…However, this study observed three main challenges in previous research studies (Bhatt and Shah, 2023;El Lel et al, 2023;Mabrouk et al, 2022;Visuña et al, 2022;Win et al, 2021;Deb and Jha, 2020). The first challenge is the availability of diverse and sufficient data is crucial for the success of ensemble CNNs.…”

“…In a similar El Lel et al (2023) proposed eleven ensemble CNNs (etc., random forest classifier, logistic regression, k-neighbors classifier, and majority voting) consisting of six CNNs and a classifier layer to classify the input image into COVID-19 or normal. Through rigorous evaluation, the logistic regression ensemble model emerged as the most effective in terms of COVID-19 detection accuracy, achieving an impressive accuracy rate of 96.29%.…”

“…Ensemble models gained significant attention in disease diagnosis due to their ability to enhance generalization performance compared to individual CNNs and other machine-learning approaches (El Lel et al, 2023;Prakash et al, 2023;Mabrouk et al, 2022). However, ensemble models face unique challenges in this field (Bhatt and Shah, 2023;El Lel et al, 2023;Mabrouk et al, 2022;Visuña et al, 2022;Win et al, 2021;Deb and Jha, 2020).…”