Date Log
Submitted
September 29, 2022
Published
September 29, 2022
Automatic Detection of COVID-19 in Digital Thorax Image with Convolutional Neural Network
Corresponding Author(s) : Andi Nur Intan Wulandari
andiintan81@gmail.com
International Journal of Allied Medical Sciences and Clinical Research,
Vol. 10 No. 3 (2022): 2022 Volume -10 - Issue 3
Abstract
COVID-19 is an infectious disease caused by the corona virus. Gold standard diagnosis of COVID-19 is RT-PCR. Alternative chest radiography for the diagnosis of COVID-19 plays a role in initial screening. The weakness of chest radiography is the lack of sensitivity in detecting COVID-19. Deep learning CNN AI technology has the ability to represent features to make predictions of the same diagnosis as radiologist through pattern recognition in digital images. Deep learning CNN that is able to classify the results of thorax radiographs automatically. Type research was analytic observational cross-sectional approach. Building a CNN deep learning architecture through the matlab R2018b program. Data is collected from CNN testing by measuring deep learning and ROC performance. Data analysis using diagnostic test with STATA statistical program. Research with 76 samples obtained by deep learning CNN is feasible and highly detects COVID-19 with an AUC value of 0.9232. Deep learning CNN is effective in detecting both COVID-19 and normal thorax images provided that the diagnosis decision still refers to the justification verification of a radiologist.
Keywords
COVID-19, thorax radiograph, deep learning CNN, automatic detection.
Andi Nur Intan Wulandari, Donny Kristanto Mulyantoro, & Dwi Rochmayanti. (2022). Automatic Detection of COVID-19 in Digital Thorax Image with Convolutional Neural Network. International Journal of Allied Medical Sciences and Clinical Research, 10(3), 353–359. https://doi.org/10.61096/ijamscr.v10.iss3.2022.353-359
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References
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References
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[11] Z. Y. Zu et al., “Coronavirus Disease 2019 (COVID-19): A Perspective from China,” RSNA.org, vol. 2019, 2019.
[12] B. J. Erickson, P. Korfiatis, Z. Akkus, and T. L. Kline, “Machine learning for medical imaging,” Radiographics, vol. 37, no. 2, pp. 505–515, 2017, doi: 10.1148/rg.2017160130.
[13] R. Primartha, Belajar Machine Learning Teori dan Praktik. Bandung: Informatika Bandung, 2018.
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[15] S. Trebeschi et al., “Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric,” Sci. Rep., no. March, pp. 1–9, 2017, doi: 10.1038/s41598-017-05728-9.
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[18] H. Polat and H. D. Mehr, “Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture,” Appl. Sci., vol. 9, no. 5, 2019, doi: 10.3390/app9050940.
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[2] et al Guan W-J, Ni Z-Y, Hu Y, Liang W-H, Ou C-Q, He J-X, “Clinical characteristics of 2019 novel coronavirus infection in China,” CC-BY-NC-ND 4.0 Int. Licens., 2020, doi: https://doi.org/10.1101/2020.02.06.20020974.
[3] Y. H. et al. N. Chen, M. Zhou, X. Dong, J. Qu, F. Gong, “Epidemiological and clinical characteristics of 99 cases of 2019 novel coronavirus pneumonia in Wuhan , China?: a descriptive study,” Elseiver Ltd., no. January, pp. 507–513, 2020.
[4] Q. Li et al., “Early Transmission Dynamics in Wuhan, China, of Novel Coronavirus–Infected Pneumonia,” N. Engl. J. Med., vol. 382, no. 13, pp. 1199–1207, 2020, doi: 10.1056/nejmoa2001316.
[5] W. L. et al. T. Ai, Z. Yang, H. Hou, C. Zhan, C. Chen, “Correlation of chest CT and RT-PCR testing in coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases,” Radiol. Soc. North Am., vol. 2019, 2019, doi: 10.1148/radiol.2020200642.
[6] L. Wang, Y. Wang, D. Ye, and Q. Liu, “Review of the 2019 novel coronavirus ( SARS-CoV-2 ) based on current evidence,” Int. J. Antimicrob. Agents, vol. 55, no. 6, p. 105948, 2020, doi: 10.1016/j.ijantimicag.2020.105948.
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[8] Y. S. Hariyani, S. Hadiyono, and T. S. Siadari, “Deteksi Penyakit Covid-19 Berdasarkan Citra X-Ray Menggunakan Deep Residual Network,” ELKOMIKA J. Tek. Energi Elektr. Tek. Telekomun. Tek. Elektron., vol. 8, no. 2, p. 443, 2020, doi: 10.26760/elkomika.v8i2.443.
[9] T. Ozturk, M. Talo, E. Azra, U. Baran, and O. Yildirim, “Automated Detection of COVID-19 Cases Using Deep Neural Networks with X-ray Images,” Comput. Biol. Med., no. January, 2020.
[10] Correspondence, “Artificial intelligence in medical imaging: Game over for radiologists?,” vol. 126, no. February, p. 108940, 2020, doi: 10.1016/j.ejrad.2020.108940.
[11] Z. Y. Zu et al., “Coronavirus Disease 2019 (COVID-19): A Perspective from China,” RSNA.org, vol. 2019, 2019.
[12] B. J. Erickson, P. Korfiatis, Z. Akkus, and T. L. Kline, “Machine learning for medical imaging,” Radiographics, vol. 37, no. 2, pp. 505–515, 2017, doi: 10.1148/rg.2017160130.
[13] R. Primartha, Belajar Machine Learning Teori dan Praktik. Bandung: Informatika Bandung, 2018.
[14] J. Lee et al., “Deep Learning in Medical Imaging?: General Overview,” Korean J. Radiol., vol. 18, no. 4, pp. 570–584, 2017.
[15] S. Trebeschi et al., “Deep Learning for Fully-Automated Localization and Segmentation of Rectal Cancer on Multiparametric,” Sci. Rep., no. March, pp. 1–9, 2017, doi: 10.1038/s41598-017-05728-9.
[16] J. T. Norweck et al., “ACR – AAPM – SIIM Technical Standard for Electronic Practice of Medical Imaging,” J Digit Imaging, pp. 38–52, 2013, doi: 10.1007/s10278-012-9522-2.
[17] Darma Putra; Westriningsih, Pengolahan Citra Digital. 2010.
[18] H. Polat and H. D. Mehr, “Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture,” Appl. Sci., vol. 9, no. 5, 2019, doi: 10.3390/app9050940.
[19] S. Saha, “A Comprehensive Guide to Convolutional Neural Networks,” Toward Data Science, 2018. https://towardsdatascience.com/a-comprehensive-guide-to-convolutional-neural-networks-the-eli5-way-3bd2b1164a53.
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