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Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM

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

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Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM. / Nasiri, Hamid; Kheyroddin, Ghazal; Dorrigiv, Morteza et al.
2022 IEEE World AI IoT Congress, AIIoT 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 201-206 (2022 IEEE World AI IoT Congress, AIIoT 2022).

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

Harvard

Nasiri, H, Kheyroddin, G, Dorrigiv, M, Esmaeili, M, Nafchi, AR, Ghorbani, MH & Zarkesh-Ha, P 2022, Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM. in 2022 IEEE World AI IoT Congress, AIIoT 2022. 2022 IEEE World AI IoT Congress, AIIoT 2022, Institute of Electrical and Electronics Engineers Inc., pp. 201-206, 2022 IEEE World AI IoT Congress, AIIoT 2022, Seattle, United States, 6/06/22. https://doi.org/10.1109/AIIoT54504.2022.9817375

APA

Nasiri, H., Kheyroddin, G., Dorrigiv, M., Esmaeili, M., Nafchi, A. R., Ghorbani, M. H., & Zarkesh-Ha, P. (2022). Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM. In 2022 IEEE World AI IoT Congress, AIIoT 2022 (pp. 201-206). (2022 IEEE World AI IoT Congress, AIIoT 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/AIIoT54504.2022.9817375

Vancouver

Nasiri H, Kheyroddin G, Dorrigiv M, Esmaeili M, Nafchi AR, Ghorbani MH et al. Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM. In 2022 IEEE World AI IoT Congress, AIIoT 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 201-206. (2022 IEEE World AI IoT Congress, AIIoT 2022). doi: 10.1109/AIIoT54504.2022.9817375

Author

Nasiri, Hamid ; Kheyroddin, Ghazal ; Dorrigiv, Morteza et al. / Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM. 2022 IEEE World AI IoT Congress, AIIoT 2022. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 201-206 (2022 IEEE World AI IoT Congress, AIIoT 2022).

Bibtex

@inproceedings{c89b9feb16e64a49a0bd451756dd620f,
title = "Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM",
abstract = "The COVID-19 disease was first discovered in Wuhan, China, and spread quickly worldwide. After the COVID-19 pandemic, many researchers have begun to identify a way to diagnose the COVID-19 using chest X-ray images. The early diagnosis of this disease can significantly impact the treatment process. In this article, we propose a new technique that is faster and more accurate than the other methods reported in the literature. The proposed method uses a combination of DenseNet169 and MobileNet Deep Neural Networks to extract the features of the patient's X-ray images. Using the univariate feature selection algorithm, we refined the features for the most important ones. Then we applied the selected features as input to the LightGBM (Light Gradient Boosting Machine) algorithm for classification. To assess the effectiveness of the proposed method, the ChestX-ray8 dataset, which includes 1125 X-ray images of the patient's chest, was used. The proposed method achieved 98.54% and 91.11% accuracies in the two-class (COVID-19, Healthy) and multi-class (COVID-19, Healthy, Pneumonia) classification problems, respectively. It is worth mentioning that we have used Gradient-weighted Class Activation Mapping (Grad-CAM) for future analysis.",
keywords = "COVID-19, DenseNet169, GradCAM, Light-GBM, MobileNet, Univariate Feature Selection",
author = "Hamid Nasiri and Ghazal Kheyroddin and Morteza Dorrigiv and Mona Esmaeili and Nafchi, {Amir Raeisi} and Ghorbani, {Mohsen Haji} and Payman Zarkesh-Ha",
year = "2022",
doi = "10.1109/AIIoT54504.2022.9817375",
language = "English",
series = "2022 IEEE World AI IoT Congress, AIIoT 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "201--206",
booktitle = "2022 IEEE World AI IoT Congress, AIIoT 2022",
note = "2022 IEEE World AI IoT Congress, AIIoT 2022 ; Conference date: 06-06-2022 Through 09-06-2022",

}

RIS

TY - GEN

T1 - Classification of COVID-19 in Chest X-ray Images Using Fusion of Deep Features and LightGBM

AU - Nasiri, Hamid

AU - Kheyroddin, Ghazal

AU - Dorrigiv, Morteza

AU - Esmaeili, Mona

AU - Nafchi, Amir Raeisi

AU - Ghorbani, Mohsen Haji

AU - Zarkesh-Ha, Payman

PY - 2022

Y1 - 2022

N2 - The COVID-19 disease was first discovered in Wuhan, China, and spread quickly worldwide. After the COVID-19 pandemic, many researchers have begun to identify a way to diagnose the COVID-19 using chest X-ray images. The early diagnosis of this disease can significantly impact the treatment process. In this article, we propose a new technique that is faster and more accurate than the other methods reported in the literature. The proposed method uses a combination of DenseNet169 and MobileNet Deep Neural Networks to extract the features of the patient's X-ray images. Using the univariate feature selection algorithm, we refined the features for the most important ones. Then we applied the selected features as input to the LightGBM (Light Gradient Boosting Machine) algorithm for classification. To assess the effectiveness of the proposed method, the ChestX-ray8 dataset, which includes 1125 X-ray images of the patient's chest, was used. The proposed method achieved 98.54% and 91.11% accuracies in the two-class (COVID-19, Healthy) and multi-class (COVID-19, Healthy, Pneumonia) classification problems, respectively. It is worth mentioning that we have used Gradient-weighted Class Activation Mapping (Grad-CAM) for future analysis.

AB - The COVID-19 disease was first discovered in Wuhan, China, and spread quickly worldwide. After the COVID-19 pandemic, many researchers have begun to identify a way to diagnose the COVID-19 using chest X-ray images. The early diagnosis of this disease can significantly impact the treatment process. In this article, we propose a new technique that is faster and more accurate than the other methods reported in the literature. The proposed method uses a combination of DenseNet169 and MobileNet Deep Neural Networks to extract the features of the patient's X-ray images. Using the univariate feature selection algorithm, we refined the features for the most important ones. Then we applied the selected features as input to the LightGBM (Light Gradient Boosting Machine) algorithm for classification. To assess the effectiveness of the proposed method, the ChestX-ray8 dataset, which includes 1125 X-ray images of the patient's chest, was used. The proposed method achieved 98.54% and 91.11% accuracies in the two-class (COVID-19, Healthy) and multi-class (COVID-19, Healthy, Pneumonia) classification problems, respectively. It is worth mentioning that we have used Gradient-weighted Class Activation Mapping (Grad-CAM) for future analysis.

KW - COVID-19

KW - DenseNet169

KW - GradCAM

KW - Light-GBM

KW - MobileNet

KW - Univariate Feature Selection

U2 - 10.1109/AIIoT54504.2022.9817375

DO - 10.1109/AIIoT54504.2022.9817375

M3 - Conference contribution/Paper

AN - SCOPUS:85134880682

T3 - 2022 IEEE World AI IoT Congress, AIIoT 2022

SP - 201

EP - 206

BT - 2022 IEEE World AI IoT Congress, AIIoT 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE World AI IoT Congress, AIIoT 2022

Y2 - 6 June 2022 through 9 June 2022

ER -