Diagnosis of COVID-19 in CT image using CNN and XGBoost

Abstract

Coronavirus disease (COVID-19) has infected more than 3.6 million people worldwide and it is responsible for more than 250,000 deaths. A major problem faced in the diagnosis of COVID-19 is the inefficiency and scarcity of medical tests. The use of computed tomography (CT) has shown promise for the evaluation of patients with suspected COVID-19 infection. CT exam analysis is complex and requires specialist effort, which can lead to diagnostic errors. The use of CAD systems can minimize the problems generated by the analysis of CTs by specialists. This paper presents a methodology for diagnosing COVID-19 using convolutional neural network (CNN) for feature extraction in CT exams and its classification using XGBoost. The methodology consists of using a CNN to extract features from 708 CTs, 312 with COVID-19, and 396 Non-COVID-19. After the extracted data, we used XGBoost for classification. The results show an accuracy of 95.07, recall of 95.09, precision of 94.99, F-score of 95, AUC of 95, and a kappa index of 90. The results obtained show that the proposed methodology can be used as a diagnostic aid system by specialists.