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Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM

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Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM. / Ezzoddin, Mobina; Nasiri, Hamid; Dorrigiv, Morteza.
Proceedings of 2022 12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022. IEEE Computer Society Press, 2022. (Iranian Conference on Machine Vision and Image Processing, MVIP; Vol. 2022-February).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Ezzoddin, M, Nasiri, H & Dorrigiv, M 2022, Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM. in Proceedings of 2022 12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022. Iranian Conference on Machine Vision and Image Processing, MVIP, vol. 2022-February, IEEE Computer Society Press, 12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022, Ahvaz, Iran, Islamic Republic of, 23/02/22. https://doi.org/10.1109/MVIP53647.2022.9738760

APA

Ezzoddin, M., Nasiri, H., & Dorrigiv, M. (2022). Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM. In Proceedings of 2022 12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022 (Iranian Conference on Machine Vision and Image Processing, MVIP; Vol. 2022-February). IEEE Computer Society Press. https://doi.org/10.1109/MVIP53647.2022.9738760

Vancouver

Ezzoddin M, Nasiri H, Dorrigiv M. Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM. In Proceedings of 2022 12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022. IEEE Computer Society Press. 2022. (Iranian Conference on Machine Vision and Image Processing, MVIP). doi: 10.1109/MVIP53647.2022.9738760

Author

Ezzoddin, Mobina ; Nasiri, Hamid ; Dorrigiv, Morteza. / Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM. Proceedings of 2022 12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022. IEEE Computer Society Press, 2022. (Iranian Conference on Machine Vision and Image Processing, MVIP).

Bibtex

@inproceedings{b060374659134fd6aa50f87826a44d65,
title = "Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM",
abstract = "The Coronavirus was detected in Wuhan, China in late 2019 and then led to a pandemic with a rapid worldwide outbreak. The number of infected people has been swiftly increasing since then. Therefore, in this study, an attempt was made to propose a new and efficient method for automatic diagnosis of Corona disease from X-ray images using Deep Neural Networks (DNNs). In the proposed method, the DensNet169 was used to extract the features of the patients' Chest X-Ray (CXR) images. The extracted features were given to a feature selection algorithm (i.e., ANOVA) to select a number of them. Finally, the selected features were classified by LightGBM algorithm. The proposed approach was evaluated on the ChestX-ray8 dataset and reached 99.20% and 94.22% accuracies in the two-class (i.e., COVID-19 and No-findings) and multi-class (i.e., COVID-19, Pneumonia, and No-findings) classification problems, respectively.",
keywords = "ANOVA, Chest X-ray Images, Coronavirus, COVID-19, DenseNet169, LightGBM",
author = "Mobina Ezzoddin and Hamid Nasiri and Morteza Dorrigiv",
year = "2022",
doi = "10.1109/MVIP53647.2022.9738760",
language = "English",
series = "Iranian Conference on Machine Vision and Image Processing, MVIP",
publisher = "IEEE Computer Society Press",
booktitle = "Proceedings of 2022 12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022",
note = "12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022 ; Conference date: 23-02-2022 Through 24-02-2022",

}

RIS

TY - GEN

T1 - Diagnosis of COVID-19 Cases from Chest X-ray Images Using Deep Neural Network and LightGBM

AU - Ezzoddin, Mobina

AU - Nasiri, Hamid

AU - Dorrigiv, Morteza

PY - 2022

Y1 - 2022

N2 - The Coronavirus was detected in Wuhan, China in late 2019 and then led to a pandemic with a rapid worldwide outbreak. The number of infected people has been swiftly increasing since then. Therefore, in this study, an attempt was made to propose a new and efficient method for automatic diagnosis of Corona disease from X-ray images using Deep Neural Networks (DNNs). In the proposed method, the DensNet169 was used to extract the features of the patients' Chest X-Ray (CXR) images. The extracted features were given to a feature selection algorithm (i.e., ANOVA) to select a number of them. Finally, the selected features were classified by LightGBM algorithm. The proposed approach was evaluated on the ChestX-ray8 dataset and reached 99.20% and 94.22% accuracies in the two-class (i.e., COVID-19 and No-findings) and multi-class (i.e., COVID-19, Pneumonia, and No-findings) classification problems, respectively.

AB - The Coronavirus was detected in Wuhan, China in late 2019 and then led to a pandemic with a rapid worldwide outbreak. The number of infected people has been swiftly increasing since then. Therefore, in this study, an attempt was made to propose a new and efficient method for automatic diagnosis of Corona disease from X-ray images using Deep Neural Networks (DNNs). In the proposed method, the DensNet169 was used to extract the features of the patients' Chest X-Ray (CXR) images. The extracted features were given to a feature selection algorithm (i.e., ANOVA) to select a number of them. Finally, the selected features were classified by LightGBM algorithm. The proposed approach was evaluated on the ChestX-ray8 dataset and reached 99.20% and 94.22% accuracies in the two-class (i.e., COVID-19 and No-findings) and multi-class (i.e., COVID-19, Pneumonia, and No-findings) classification problems, respectively.

KW - ANOVA

KW - Chest X-ray Images

KW - Coronavirus

KW - COVID-19

KW - DenseNet169

KW - LightGBM

U2 - 10.1109/MVIP53647.2022.9738760

DO - 10.1109/MVIP53647.2022.9738760

M3 - Conference contribution/Paper

AN - SCOPUS:85127450421

T3 - Iranian Conference on Machine Vision and Image Processing, MVIP

BT - Proceedings of 2022 12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022

PB - IEEE Computer Society Press

T2 - 12th Iranian/2nd International Conference on Machine Vision and Image Processing, MVIP 2022

Y2 - 23 February 2022 through 24 February 2022

ER -