Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - COV-ADSX
T2 - An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19
AU - Hasani, Sharif
AU - Nasiri, Hamid
PY - 2022/2/28
Y1 - 2022/2/28
N2 - 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.
AB - 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.
KW - Chest X-ray Images
KW - COVID-19
KW - Deep Neural Networks
KW - DenseNet169
KW - XGBoost
U2 - 10.1016/j.simpa.2021.100210
DO - 10.1016/j.simpa.2021.100210
M3 - Journal article
AN - SCOPUS:85122496640
VL - 11
JO - Software Impacts
JF - Software Impacts
SN - 2665-9638
M1 - 100210
ER -