Image Based Classification of COVID-19 Infection Using Ensemble of Machine Learning Classifiers and Deep Learning Techniques

Coronavirus illness (COVID-19) had a major impact on multiple areas in society including the healthcare system and the downfall of the global economy. The researchers, doctors, and specialists are working towards new techniques to identify COVID-19 more quickly, such as developing a device that can detect the COVID-19 automatically. In this paper, we propose an automated detection mechanism for identifying COVID-19 patients using a patient's chest X-ray images. The proposed system made use of CNN (convolutional neural network) and an ensemble of a set of classifiers. The CNN is used for feature extraction in the training and input image whereas classifiers are used for effective prediction. Some of the binary ML (machine learning) classifiers are used for the identification of COVID-19 based on the retrieved characteristics. Later these results are grouped to create a pool of ensemble of classifiers to assure superior results considering various datasets of different sized images with varying resolutions. The performance analysis is discussed and shown as it is better than other previous schemes using deep learning, with 99.17 percent accuracy, 99.19 percent precision, 99.17 percent recall, and 99.43 percent F1 score. The system's high value in the automated detection of COVID-19 is maintained due to its quick identification and low false-negative rate. © 2022 IEEE.

Assessing Hospitalization for SARS-CoV-2 Confirmed Cases by a Cross-Entropy Weighted Ensemble Classifier

The potential need of hospitalization for patients with acute respiratory COVID-19 infection caused by the SARS-CoV2 virus is a critical decision, as it has a direct effect on the potential response. In addition, it leads to an allocation of resources (bed, care, and medical personnel) that, given the pandemic, are limited. According to official information reported since March 1, 2020 and updated to June 30, 2021, an ensemble of classifiers weighted by the cross-entropy information measure is proposed. We considered data based on the knowledge of a set of features before a wide availability of vaccines or identified variants of the virus were present. The aim is to contribute toward the enhancement of a better-informed assessment of risk by the general population when exposed to the disease in the aforementioned period. The results show an improvement in the detection of cases susceptible to hospitalization, with an accuracy of 91.46%, and in a restrictive scenario, there is a preventive alert to patients, even though under the established criteria should not be admitted, to remain under monitoring to anticipate the evolution of the disease to a severe stage. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Optimized Fusion of CNNs to Diagnose Pulmonary Diseases on Chest X-Rays

Since the beginning of the COVID-19 pandemic, more than 350 million cases and 5 million deaths have occurred. Since day one, multiple methods have been provided to diagnose patients who have been infected. Alongside the gold standard of laboratory analyses, deep learning algorithms on chest X-rays (CXR) have been developed to support the COVID-19 diagnosis. The literature reports that convolutional neural networks (CNNs) have obtained excellent results on image datasets when the tests are performed in cross-validation, but such models fail to generalize to unseen data. To overcome this limitation, we exploit the strength of multiple CNNs by building an ensemble of classifiers via an optimized late fusion approach. To demonstrate the system’s robustness, we present different experiments on open source CXR datasets to simulate a real-world scenario, where scans of patients affected by various lung pathologies and coming from external datasets are tested. Promising performances are obtained both in cross-validation and in external validation, obtaining an average accuracy of 93.02% and 91.02%, respectively. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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

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.