tetano
Editor, Senior Moderator
Radiography (Lond)
. 2022 Mar 28;S1078-8174(22)00043-8.
doi: 10.1016/j.radi.2022.03.011. Online ahead of print.
Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost
H Nasiri[SUP] 1 [/SUP], S Hasani[SUP] 2 [/SUP]
Affiliations
Abstract
Introduction: In late 2019 and after the COVID-19 pandemic in the world, many researchers and scholars tried to provide methods for detecting COVID-19 cases. Accordingly, this study focused on identifying patients with COVID-19 from chest X-ray images.
Methods: In this paper, a method for diagnosing coronavirus disease from X-ray images was developed. In this method, DenseNet169 Deep Neural Network (DNN) was used to extract the features of X-ray images taken from the patients' chests. The extracted features were then given as input to the Extreme Gradient Boosting (XGBoost) algorithm to perform the classification task.
Results: Evaluation of the proposed approach and its comparison with the methods presented in recent years revealed that this method was more accurate and faster than the existing ones and had an acceptable performance for detecting COVID-19 cases from X-ray images. The experiments showed 98.23% and 89.70% accuracy, 99.78% and 100% specificity, 92.08% and 95.20% sensitivity in two and three-class problems, respectively.
Conclusion: This study aimed to detect people with COVID-19, focusing on non-clinical approaches. The developed method could be employed as an initial detection tool to assist the radiologists in more accurate and faster diagnosing the disease.
Implication for practice: The proposed method's simple implementation, along with its acceptable accuracy, allows it to be used in COVID-19 diagnosis. Moreover, the gradient-based class activation mapping (Grad-CAM) can be used to represent the deep neural network's decision area on a heatmap. Radiologists might use this heatmap to evaluate the chest area more accurately.
Keywords: COVID-19; Chest X-ray images; Deep neural network (DNN); DenseNet169; XGBoost.
. 2022 Mar 28;S1078-8174(22)00043-8.
doi: 10.1016/j.radi.2022.03.011. Online ahead of print.
Automated detection of COVID-19 cases from chest X-ray images using deep neural network and XGBoost
H Nasiri[SUP] 1 [/SUP], S Hasani[SUP] 2 [/SUP]
Affiliations
- PMID: 35410707
- DOI: 10.1016/j.radi.2022.03.011
Abstract
Introduction: In late 2019 and after the COVID-19 pandemic in the world, many researchers and scholars tried to provide methods for detecting COVID-19 cases. Accordingly, this study focused on identifying patients with COVID-19 from chest X-ray images.
Methods: In this paper, a method for diagnosing coronavirus disease from X-ray images was developed. In this method, DenseNet169 Deep Neural Network (DNN) was used to extract the features of X-ray images taken from the patients' chests. The extracted features were then given as input to the Extreme Gradient Boosting (XGBoost) algorithm to perform the classification task.
Results: Evaluation of the proposed approach and its comparison with the methods presented in recent years revealed that this method was more accurate and faster than the existing ones and had an acceptable performance for detecting COVID-19 cases from X-ray images. The experiments showed 98.23% and 89.70% accuracy, 99.78% and 100% specificity, 92.08% and 95.20% sensitivity in two and three-class problems, respectively.
Conclusion: This study aimed to detect people with COVID-19, focusing on non-clinical approaches. The developed method could be employed as an initial detection tool to assist the radiologists in more accurate and faster diagnosing the disease.
Implication for practice: The proposed method's simple implementation, along with its acceptable accuracy, allows it to be used in COVID-19 diagnosis. Moreover, the gradient-based class activation mapping (Grad-CAM) can be used to represent the deep neural network's decision area on a heatmap. Radiologists might use this heatmap to evaluate the chest area more accurately.
Keywords: COVID-19; Chest X-ray images; Deep neural network (DNN); DenseNet169; XGBoost.