Ensemble of Convolutional Neural Networks for COVID-19 Localization on Chest X-ray Images

Abstract

Coronavirus disease (COVID-19) is caused by the SARS-CoV-2 virus and has been declared as a pandemic. The early detection of COVID-19 is necessary to interrupt the spread of the virus and prevent its transmission. X-rays and CT scans can assist radiologists in disease detection. However, detecting COVID-19 on chest radiographs is challenging due to similarities with other bacterial and viral pneumonias. Therefore, it is essential to develop a fast and accurate algorithm for detecting COVID-19. In this work, we applied pre-processing in order to increase the contrast in X-rays. We then use the ResNet-50 model to differentiate between normal and COVID-19 images. Images classified as COVID-19 were investigated with an ensemble detection model (deep learning models—You Only Look Once version 5 and X). The classification model achieved an accuracy of 0.864 and an AUC of 0.904 in 5-fold cross-validation. The overlap between the predicted bounding boxes and the ground truth reached, in the ensemble model, a mAP of 59.63% in 5-fold cross-validation. Thus, we consider that the result was significant in terms of the global classification of the images, as well as in the location of suspicious regions that require greater attention from the specialist, which makes the developed model a fast and promising way to aid the specialist in decision making.

Keywords

• COVID-19
• abnormality classification
• localization
• lung opacity detection
• deep neural network
• chest X-ray