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

Sharif Hasani; Hamid Nasiri

doi:10.1016/j.simpa.2021.100210

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

Sharif Hasani, Hamid Nasiri

Research output: Contribution to journal › Article › peer-review

15 Citations (Scopus)

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.

Original language	English
Article number	100210
Number of pages	3
Journal	Software Impacts
Volume	11
Early online date	10 Jan 2022
DOIs	https://doi.org/10.1016/j.simpa.2021.100210
Publication status	Published - 1 Feb 2022
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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10.1016/j.simpa.2021.100210Licence: CC BY

Cite this

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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.",

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author = "Sharif Hasani and Hamid Nasiri",

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doi = "10.1016/j.simpa.2021.100210",

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AU - Nasiri, Hamid

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Y1 - 2022/2/1

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

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DO - 10.1016/j.simpa.2021.100210

M3 - Article

AN - SCOPUS:85122496640

SN - 2665-9638

VL - 11

JO - Software Impacts

JF - Software Impacts

M1 - 100210

ER -

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

Abstract

UN SDGs

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