A Two-Headed Deep Learning Framework for Predicting Severity of COVID-19 Disease

Çoşku Öksüz; Oğuzhan Urhan; Mehmet Kemal Güllü

A Two-Headed Deep Learning Framework for Predicting Severity of COVID-19 Disease

Abstract

Introduction-Objectives: The high contagiousness of the SARS-COV-2 virus has resulted in many people being infected worldwide. In many countries, the capacity of intensive care units has been insufficient and has become unable to accept new patients. Imaging-based non-invasive methods developed as an alternative to the RT-PCR technique to control the spread of the virus during the pandemic process generally focus on the presence or absence of the disease. However, these methods do not provide information about how severe the disease is and how it progresses. Therefore, in this study, a deep learning-based estimation framework with low computational load is proposed to predict severity scores using chest radiographs. Materials-Methods: The pre-trained ImageNet models are used as feature extraction networks to extract generic features. A two-headed estimation subnetwork each with the same number of layers is created to learn taskspecific features. Eventually, an end-to-end trainable lightweight deep model is created by connecting the estimation subnetwork to the feature extraction network. Results: The proposed model is evaluated on a publicly available Cohen’s covid-chestxray-data set. The best cross-validation performance in terms of RMSE, MAE, and R2 in the prediction of lung involvement and opacity is obtained as 1.39/0.98, 1.1/0.81, 0.65/0.66, respectively. Conclusions: Although the model has been trained with limited data, promising results are achieved with an end-to-end framework for estimating the severity of the COVID-19 disease.

Keywords

References

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Details

Primary Language

English

Subjects

Clinical Sciences, Engineering

Journal Section

Research Article

Authors

Çoşku Öksüz ^* This is me
Türkiye

Oğuzhan Urhan This is me
Türkiye

Mehmet Kemal Güllü This is me
Türkiye

Publication Date

September 30, 2021

Submission Date

June 30, 2021

Acceptance Date

September 25, 2021

Published in Issue

Year 2021 Volume: 1 Number: 2

IZ

https://izlik.org/JA72CP39BY

Cite

RIS / Bibtex

APA

Öksüz, Ç., Urhan, O., & Güllü, M. K. (2021). A Two-Headed Deep Learning Framework for Predicting Severity of COVID-19 Disease. Artificial Intelligence Theory and Applications, 1(2), 19-28. https://izlik.org/JA72CP39BY

AMA

1.Öksüz Ç, Urhan O, Güllü MK. A Two-Headed Deep Learning Framework for Predicting Severity of COVID-19 Disease. AITA. 2021;1(2):19-28. https://izlik.org/JA72CP39BY

Chicago

Öksüz, Çoşku, Oğuzhan Urhan, and Mehmet Kemal Güllü. 2021. “A Two-Headed Deep Learning Framework for Predicting Severity of COVID-19 Disease”. Artificial Intelligence Theory and Applications 1 (2): 19-28. https://izlik.org/JA72CP39BY.

EndNote

Öksüz Ç, Urhan O, Güllü MK (September 1, 2021) A Two-Headed Deep Learning Framework for Predicting Severity of COVID-19 Disease. Artificial Intelligence Theory and Applications 1 2 19–28.

IEEE

[1]Ç. Öksüz, O. Urhan, and M. K. Güllü, “A Two-Headed Deep Learning Framework for Predicting Severity of COVID-19 Disease”, AITA, vol. 1, no. 2, pp. 19–28, Sept. 2021, [Online]. Available: https://izlik.org/JA72CP39BY

ISNAD

Öksüz, Çoşku - Urhan, Oğuzhan - Güllü, Mehmet Kemal. “A Two-Headed Deep Learning Framework for Predicting Severity of COVID-19 Disease”. Artificial Intelligence Theory and Applications 1/2 (September 1, 2021): 19-28. https://izlik.org/JA72CP39BY.

JAMA

1.Öksüz Ç, Urhan O, Güllü MK. A Two-Headed Deep Learning Framework for Predicting Severity of COVID-19 Disease. AITA. 2021;1:19–28.

MLA

Öksüz, Çoşku, et al. “A Two-Headed Deep Learning Framework for Predicting Severity of COVID-19 Disease”. Artificial Intelligence Theory and Applications, vol. 1, no. 2, Sept. 2021, pp. 19-28, https://izlik.org/JA72CP39BY.

Vancouver

1.Çoşku Öksüz, Oğuzhan Urhan, Mehmet Kemal Güllü. A Two-Headed Deep Learning Framework for Predicting Severity of COVID-19 Disease. AITA [Internet]. 2021 Sep. 1;1(2):19-28. Available from: https://izlik.org/JA72CP39BY