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COV-ADSX: An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19

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COV-ADSX: An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19. / Hasani, Sharif; Nasiri, Hamid.
In: Software Impacts, Vol. 11, 100210, 28.02.2022.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

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Hasani S, Nasiri H. COV-ADSX: An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19. Software Impacts. 2022 Feb 28;11:100210. Epub 2022 Jan 10. doi: 10.1016/j.simpa.2021.100210

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Bibtex

@article{db7b7f3044574e96b3ee686a4b0e3bf7,
title = "COV-ADSX: An Automated Detection System using X-ray Images, Deep Learning, and XGBoost for COVID-19",
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.",
keywords = "Chest X-ray Images, COVID-19, Deep Neural Networks, DenseNet169, XGBoost",
author = "Sharif Hasani and Hamid Nasiri",
year = "2022",
month = feb,
day = "28",
doi = "10.1016/j.simpa.2021.100210",
language = "English",
volume = "11",
journal = "Software Impacts",
issn = "2665-9638",
publisher = "Elsevier B.V.",

}

RIS

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 -