CCBlock based on deep learning for diagnosis COVID-19 in chest x-ray image

Abstract

Purpose: COVID-19 pandemic continues to hit countries one after the other and has dramatically affected the health and well-being of the world's population. With the daily increase in the number of people with this disease, the impressive speed of spread and the delay in the results of PCR analysis, it may cause the disease to spread more broadly. Therefore it is necessary to consider finding alternative methods of detection and diagnosis COVID-19 to prohibit the spread of the disease among people. Convolutional Neural Network (CNN) automated detection systems have shown auspicious results in detecting patients with COVID-19 through radiography; thus, we suggest them as an alternative option to diagnose COVID-19.Method: In this study, an early screening model based on the enhancement of classical Visual Geometry Group Network (VGG) with Convolutional Covid Block (CCBlock) was proposed to detect and distinguish COVID-19 from Pneumonia, and healthy people using chest X-ray radiographs. The data set used for model testing is the x-ray images available on public platforms, which consist of 1,828 x-ray images, including 310 images for confirmed COVID-19 patients, 864 images for pneumonia patients, and 654 images for healthy people.Results: The experiment result of the dataset showed that the added enhancements to the classical VGG network with X-ray imaging provide the highest detection performance and overall accuracy of 98.52% for two classes and 95.34% accuracy for three classes.Conclusions: Considering the achievement results obtained, it was found that utilizing the enhanced VGG deep neural network helps radiologists automatically diagnose COVID-19 in X-ray images.

Optimized Deep Transfer Learning for Covid-19 Screening using Chest X-Ray Image

“…The performance of the system is then evaluated by calculating accuracy, specificity, precision, and recall, as shown in Equations (1-4). For the "new" images, F-Measure is also calculated to replace accuracy, as shown in Equation (5). In these equations, TP is true positives, TN is true negatives, FP is false positives, and FN is false negatives.…”

“…Another example is presented in [4], in which the authors use deep features of pre-trained neural network whose output is then fed to SVM classifier. Other examples found in the literature are the use of VGG-Net with Convolution Covid Block [5], ResNet-50 [6], and U-Net with 3D deep learning [7]. All of these approaches are aimed to develop the best architectures or classifiers to achieve the highest accuracy and high processing speed.…”