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.
【저자키워드】 COVID-19, Convolutional neural network, X-ray images, Ensemble of classifiers, Automatic diagnosis, 【초록키워드】 coronavirus disease, X-ray, Features, Accuracy, False negative rate, Patient, automated, dataset, recall, automatic detection, Chest X-ray images, COVID-19 patient, Healthcare system, Support, Precision, random, AdaBoost, classifiers, detection systems, feature, researchers, identify, develop, caused, maintain, infected with COVID-19, machine learning classifier, 【제목키워드】 X-ray, COVID-19 diagnosis, machine learning classifier,