EMCNet: Automated COVID-19 diagnosis from X-ray images using convolutional neural network and ensemble of machine learning classifiers. (2021)

• Record Type: Journal Article
• Authors: Saha, Prottoy; Sadi, Muhammad Sheikh; Islam, Md. Milon
• Is Part Of: Informatics in medicine unlocked. Volume 22(2021)
• Publication Date: 2021
• Subjects: COVID-19 -- Convolutional neural network -- Ensemble of classifiers -- Automatic diagnosis -- X-ray images; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2020.100505
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: Recently, coronavirus disease (COVID-19) has caused a serious effect on the healthcare system and the overall global economy. Doctors, researchers, and experts are focusing on alternative ways for the rapid detection of COVID-19, such as the development of automatic COVID-19 detection systems. In this paper, an automated detection scheme named EMCNet was proposed to identify COVID-19 patients by evaluating chest X-ray images. A convolutional neural network was developed focusing on the simplicity of the model to extract deep and high-level features from X-ray images of patients infected with COVID-19. With the extracted features, binary machine learning classifiers (random forest, support vector machine, decision tree, and AdaBoost) were developed for the detection of COVID-19. Finally, these classifiers' outputs were combined to develop an ensemble of classifiers, which ensures better results for the dataset of various sizes and resolutions. In comparison with other recent deep learning-based systems, EMCNet showed better performance with 98.91% accuracy, 100% precision, 97.82% recall, and 98.89% F1-score. The system could maintain its great importance on the automatic detection of COVID-19 through instant detection and low false negative rate.