Model Design of The Image Recognition of Lung CT Scan for COVID-19 Detection Using Artificial Neural Network
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Abstract
COVID-19 has become a pandemic and is a big problem that needs to be checked out immediately. CT scan images can explain the lung conditions of COVID-19 patients and have the potential to be a clinical diagnostic tool. In this research, we classify COVID-19 by recognizing images on a computer tomography scan (CT scan) of the lungs using digital image processing and GLCM feature extraction techniques to obtain grayscale level values in CT images, followed by the creation of an artificial neural network model. So that the model can classify CT scan images, the results in this research obtained the most optimal model for COVID-19 classification performance with 90% accuracy, 88% precision, 91% recall, and 90% F1 score. This research can be a useful tool for clinical practitioners and radiologists to assist them in the diagnosis, quantification, and follow-up of COVID-19 cases.
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R. Bala and D. Kumar, “Classification Using ANN: A Review,” Int. J. Comput. Intell. Res., vol. 13, no. 7, pp. 1811–1820, 2017, [Online]. Available: http://www.ripublication.com.
R. Dharwal, “Applications of Artificial Neural Networks: A Review,” Indian J. Sci. Technol., vol. 9, no. 1, pp. 1–8, 2016, doi: 10.17485/ijst/2016/v9i47/106807.
L. Kaur, M. Sharma, R. Dharwal, and A. Bakshi, “Lung Cancer Detection Using CT Scan with Artificial Neural Netwok,” ICRIEECE 2018, pp. 1624–1629, 2018, doi: 10.1109/ICRIEECE44171.2018.9009244.
J. Turian, J. Bergstra, and Y. Bengio, “Quadratic features and deep architectures for chunking,” Proc. Hum. Lang. Technol. 2009 Annu. Conf. North Am. Chapter Assoc. Comput. Linguist., no. June, pp. 245--248, 2009, [Online]. Available: https://aclanthology.org/N09-2062.
C. Nwankpa, W. Ijomah, A. Gachagan, and S. Marshall, “Activation Functions: Comparison of trends in Practice and Research for Deep Learning,” pp. 1–20, 2018, [Online]. Available: http://arxiv.org/abs/1811.03378.
V. Nair and G. E. Hinton, “Rectified Linear Units Improve Restricted Boltzmann Machines,” ICML, 2010.
Y. Lecun, Y. Bengio, and G. Hinton, “Deep learning,” Nature, vol. 521, no. 7553, pp. 436–444, 2015, [Online]. Available: https: //doi.org/10.1038/nature14539.
A. L. Maas, A. Y. Hannun, and A. Y. Ng, “Rectifier nonlinearities improve neural network acoustic models,” ICML Work. Deep Learn. Audio, Speech Lang. Process., vol. 28, 2013.
D. A. Clevert, T. Unterthiner, and S. Hochreiter, “Fast and accurate deep network learning by exponential linear units (ELUs),” 4th Int. Conf. Learn. Represent. ICLR 2016 - Conf. Track Proc., pp. 1–14, 2016, [Online]. Available: http://arxiv.org/abs/1511.07289%0A[67].
D. P. Kingma and J. L. Ba, “Adam: A method for stochastic optimization,” 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc., pp. 1–15, 2015.
A. Özgür and Fatih Nar, “Effect of Dropout layer on Classical Regression Problems,” 2020 28th Signal Process. Commun. Appl. Conf., pp. 1–4, 2020, doi: 10.1109/SIU49456.2020.9302054.
M. Maftouni, A. Chung, C. Law, B. Shen, and Z. J. Kong, “A Robust Ensemble-Deep Learning Model for COVID-19 Diagnosis based on an Integrated CT Scan Images Database,” Proc. 2021 IISE Annu. Conf., 2021.
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Published
Mar 29, 2022
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How to Cite
Akbar Dwijaya, M., Ali Ahmad, U., Purwo Wijayanto, R., & Astuti Nugrahaeni, R. (2022). Model Design of The Image Recognition of Lung CT Scan for COVID-19 Detection Using Artificial Neural Network. Jurnal Nasional Teknik Elektro, 11(1), 21–28. https://doi.org/10.25077/jnte.v11n1.984.2022
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Telecommunication
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