COVID-19 classification in X-ray chest images using a new convolutional neural network: CNN-COVID

Purpose: COVID-19 causes lung inflammation and lesions, and chest X-ray and computed tomography images are remarkably suitable for differentiating the new disease from patients with other lung diseases In this paper, we propose a computer model to classify X-ray images of patients diagnosed with COVID-19 Chest X-ray exams were chosen over computed tomography scans because they are low cost, results are quickly obtained, and X-ray equipment is readily available Methods: A new CNN network, called CNN-COVID, has been developed to classify X-ray patient’s images Images from two different datasets were used The images of Dataset I is originated from the COVID-19 image data collection and the ChestXray14 repository, and the images of Dataset II belong to the BIMCV COVID-19+ repository To assess the accuracy of the network, 10 training and testing sessions were performed in both datasets A confusion matrix was generated to evaluate the model’s performance and calculate the following metrics: accuracy (ACC), sensitivity (SE), and specificity (SP) In addition, Receiver Operating Characteristic (ROC) curves and Areas Under the Curve (AUCs) were also considered Results: After running 10 tests, the average accuracy for Dataset I and Dataset II was 0 9787 and 0 9839, respectively Since the weights of the best test results were applied in the validation, it was obtained the accuracy of 0 9722 for Dataset I and 0 9884 for Dataset II Conclusions: The results showed that the CNN-COVID is a promising tool to help physicians classify chest images with pneumonia, considering pneumonia caused by COVID-19 and pneumonia due to other causes © 2021, Sociedade Brasileira de Engenharia Biomedica