ML and DL Architectures Comparisons for the Classification of COVID-19 Using Chest X-Ray Images

ABSTRACT

Pandemic COVID-19 ranked as one of the world’s worst pandemics ever witnessed in history. It has affected every country by spreading this disease with an increase in mortality at alarming rates despite the technologically advanced era of medicine. AI/ML is one of the strong wings in the medical field so while fighting the battle to control and diagnose the best medicine for the outbreak COVID-19 disease. Automated and AI-based prediction models for COVID-19 are the main attraction for the scientist hoping to support some good medical decisions at this difficult time. However, mostly classical image processing methods have been implemented to detect COVID-19 cases resultant in low accuracy. In this chapter, multiple naïve machine and deep learning architectures are implied to evaluate the performance of the models for the classification of COVID-19 using a dataset comprising of chest x-ray images of, i.e., COVID-19 patients and normal (non-infected) individuals. The analysis looks at three machine learning architectures including Logistic Regression, Decision Tree (DT) Classifier, and support vector machine (SVM), and four deep learning architectures, namely Convolutional neural networks (CNNs), VGG19, ResNet50, and AlexNet. The dataset has been divided into train, test and validation set and the same data have been used for the training, testing, and validation of all the architectures. The result analysis shows that AlexNet provides the best performance out of all the architectures. It can be seen that the AlexNet model achieved 98.05% accuracy (ACC), 97.40% recall, 98.03% F1-score, 98.68% precision, and 98.05% area under the curve (AUC) score. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.