Abstract
Following the COVID-19 pandemic, scientists have been looking for different ways to diagnose COVID-19, and these efforts have led to a variety of solutions. One of the common methods of detecting infected people is chest radiography. In this paper, an Automated Detection System using X-ray images (COV-ADSX) is proposed, which employs a deep neural network and XGBoost to detect COVID-19. COV-ADSX was implemented using the Django web framework, which allows the user to upload an X-ray image and view the results of the COVID-19 detection and image’s heatmap, which helps the expert to evaluate the chest area more accurately.
Highlights
COVID-19 virus was first reported in Wuhan, China, in late 2019 and spread rapidly throughout the world [1,2,3,4]
COV-ADSX included four main steps as follows: (1) receiving the user’s image and sending it to the deep learning model (Fig. 1); (2) extracting the features of the image using DenseNet169; (3) giving the extracted features as input to the XGBoost algorithm and performing the classification, i.e., detecting whether the person has infected with COVID-19 or not; (4) using the Gradient-based Class Activation Mapping (Grad-CAM) algorithm [26], specifying the decision area on the heatmap, and displaying it to the user (Fig. 2)
When a patient goes to the hospital and a chest X-ray (CXR) image is taken, the image automatically is given as input to the COVADSX, and the software determines if the person was infected with COVID-19 or not
Summary
COVID-19 virus was first reported in Wuhan, China, in late 2019 and spread rapidly throughout the world [1,2,3,4]. According to the previous studies [10,11,12], X-ray images of patients infected with COVID-19 have important and useful information for detecting this virus. These images were employed in the software introduced in this paper (i.e., COV-ADSX). COV-ADSX uses the algorithm proposed by Nasiri and Hasani [13], through which image features were extracted using DenseNet169 [14] Deep Neural Network (DNN), and the extracted features were given as input to the XGBoost algorithm to perform the classification. COV-ADSX receives an X-ray image of a person’s chest and uses a DNN to extract its features. It gives the extracted features to the trained XGBoost to determine if the person was infected with COVID-19 or not
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