Exploring COVID-19 Classification and Object Detection Strategies

Abstract

The overlapping imaging characteristics of COVID-19 viral pneumonia and non-COVID-19 viral pneumonia chest X-rays (CXRs) make differentiation difficult for radiologists. Machine learning (ML) has demonstrated promising outcomes in a range of medical sectors, enhancing diagnostic accuracy through its interaction with radiological tests. The potential contribution of ML models in assisting radiologists in discriminating COVID-19 from non-COVID-19 viral pneumonia from CXRs, on the other hand, deserves further examination and exploration. The goal of this study is to empirically assess ML models' capacity to classify X-ray images into COVID-19, pneumonia, and normal cases. The study evaluates the efficacy of K-nearest Neighbor (KNN), random forest (RF), AdaBoost (AB), and neural networks (NN) with various hidden neuron configurations using a wide range of performance measures. These metrics evaluate the area under the curve (AUC), classification accuracy (CA), F1 score (F1), precision, and recall, resulting in a comprehensive evaluation technique. ROC analysis is used to gain a thorough knowledge of the models' discriminating skills. The results show that NN models, particularly those with 100 and 150 hidden neurons, outperform in all criteria, proving their ability to reliably categorize medical disorders. Notably, the study emphasizes the difficulties in separating COVID-19 from pneumonia, emphasizing the importance of strong classification methods. While the study provides useful insights, its drawbacks include the use of a single dataset, the absence of more sophisticated deep learning architectures, and a lack of interpretability analyses. Nonetheless, the study adds to the developing area of medical picture categorization, directing future attempts to improve diagnosis accuracy and widen the use of machine learning in healthcare. The findings highlight the utility of NN models in medical diagnostics and pave the way for future study in this vital area of technology and healthcare.