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Ground glass opacity detection convolutional neural network method efficient net-b0 architecture in corona virus disease 2019 cases
Corresponding Author(s) : Dwi Ajeng Risqy Hasanah Syam
International Journal of Allied Medical Sciences and Clinical Research,
Vol. 12 No. 1 (2024): 2024 Volume -12 - Issue 1
Abstract
COVID-19 is an infectious disease caused by a new type of coronavirus. The gold standard diagnosis of COVID-19 is a PCR test. Thorax radiographic examination is an alternative to the diagnosis of COVID-19 against initial screening. Thorax radiography sensitivity is lacking in GGO detection of COVID-19. CNN's deep learning architecture EficientNet-B0 has the ability to represent the same diagnostic prediction features as radiologists. The purpose of this study resulted in CNN's EfficientNet-B0 architecture that is able to classify radiographic images of the thorax automatically in detecting GGO COVID- 19 and has similar results with the results of the doctor's experiment. This type of research is a quasi-experimental design Post-test Only Control Group Design. Build the CNN EfficientNet-B0 architecture through the MATLAB R2021a program. The sample totaled 78 radiographic images of the thorax. The data analysis is a statistical test of Chi-Square. Research proves CNN's performance of the EfficientNet-B0 architecture is excellent in detecting GGO COVID-19 with an AUC value of 0.994 range (0.90-1.00). The deep learning model can be considered as an alternative tool for establishing a diagnosis of COVID-19 with the condition that the verification of the diagnosis decision remains based on the confirmation and justification of the radiologist.
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- WHO, “Coronavirus disease (COVID-19),” World Health Organization, 2021. https://www.who.int/health- topics/coronavirus#tab=tab_1 (accessed Sep. 24, 2022).
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References
WHO, “Coronavirus disease (COVID-19),” World Health Organization, 2021. https://www.who.int/health- topics/coronavirus#tab=tab_1 (accessed Sep. 24, 2022).
Rafikasari A. Formulating Indonesia’s Covid-19 Policy based on South Korea’s Experience. Journal of Humanities and Education Development. 2020;2(3):170-6. https://dx.doi.org/10.22161/jhed.2.3.3
Wang W, Xu Y, Gao R, Lu R, Han K, Wu G, Tan W. Detection of SARS-CoV-2 in different types of clinical specimens. Jama. 2020 May 12;323(18):1843-4. doi:10.1001/jama.2020.3786
Narin A, Kaya C, Pamuk Z. Automatic detection of coronavirus disease (covid-19) using x-ray images and deep convolutional neural networks. Pattern Analysis and Applications. 2021 Aug;24:1207-20. doi: 10.1007/s10044-021-00984-y.
Wu Z, McGoogan JM. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72 314 cases from the Chinese Center for Disease Control and Prevention. jama. 2020 Apr 7;323(13):1239-42. doi: 10.1001/jama.2020.2648.
Amelia N. Deteksi Covid-19 Berdasarkan Citra Chest X-Ray Menggunakan Support Vector Machine. MATHunesa: Jurnal Ilmiah Matematika. 2021 Dec 31;9(3):494-500. doi: 10.26740/mathunesa.v9n3.p494-500.
Wang Y, Kang H, Liu X, Tong Z. Combination of RT‐qPCR testing and clinical features for diagnosis of COVID‐19 facilitates management of SARS‐CoV‐2 outbreak. Journal of medical virology. 2020 Jun;92(6):538. doi: 10.1002/jmv.25721.
Jindal H, Jain S, Suvvari TK, Kutikuppala L, Rackimuthu S, Rocha IC, Goyal S, Radha. False-negative RT-PCR findings and double mutant variant as factors of an overwhelming second wave of COVID-19 in India: an emerging global health disaster. SN comprehensive clinical medicine. 2021 Dec;3:2383-8. doi: 10.1007/s42399-021-01059-z.
Ai T, Yang Z, Hou H, Zhan C, Chen C, Lv W, Tao Q, Sun Z, Xia L. Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology. 2020 Aug;296(2):E32-40. doi: 10.14358/PERS.80.2.000.
Shrestha R, Shrestha L. Coronavirus disease 2019 (Covid-19): A pediatric perspective. JNMA: Journal of the Nepal Medical Association. 2020 Jul;58(227):525. doi: 10.31729/jnma.4977.
WHO, “Coronavirus disease (COVID-19) technical guidance: Patient management,” World Health Organization, 2020. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/technical-guidance/patient-management (accessed Sep. 26, 2022).
Bernheim A, Mei X, Huang M, Yang Y, Fayad ZA, Zhang N, Diao K, Lin B, Zhu X, Li K, Li S. Chest CT findings in coronavirus disease-19 (COVID-19): relationship to duration of infection. Radiology. 2020 Jun;295(3):685-91. https://doi.org/10.1148/radiol.2020200463
Pan F, Ye T, Sun P, Gui S, Liang B, Li L, Zheng D, Wang J, Hesketh RL, Yang L, Zheng C. Time course of lung changes at chest CT during recovery from coronavirus disease 2019 (COVID-19). Radiology. 2020 Jun;295(3):715-21. https://pubs.rsna.org/doi/abs/10.1148/radiol.2020200370
Lan L, Xu D, Ye G, Xia C, Wang S, Li Y, Xu H. Positive RT-PCR test results in patients recovered from COVID-19. Jama. 2020 Apr 21;323(15):1502-3. doi: 10.1001/jama.2020.2783.
Ozturk T, Talo M, Yildirim EA, Baloglu UB, Yildirim O, Acharya UR. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in biology and medicine. 2020 Jun 1;121:103792. doi: 10.1016/j.compbiomed.2020.103792.
M. Imron, M. N. Widyawati, and S. Masrochah, Tuberculosis detection using convolutional neural network. Int. J. Allied Med. Sci. Clin. Res., 2020; 8(3):535–546 https://ijamscr.com/ijamscr/article/view/892
Yudistira N, Widodo AW, Rahayudi B. Deteksi Covid-19 pada citra sinar-x dada menggunakan deep learning yang efisien. Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK). 2020 Dec;7(6):1289-96. doi: 10.25126/jtiik.202073651.
Caobelli F. Artificial intelligence in medical imaging: Game over for radiologists?. European journal of radiology. 2020 May 1;126. doi: 10.1016/j.ejrad.2020.108940.
Erickson BJ, Korfiatis P, Akkus Z, Kline TL. Machine learning for medical imaging. radiographics. 2017 Mar;37(2):505-15. https://pubs.rsna.org/doi/abs/10.1148/rg.2017160130
Baranwal SK, Jaiswal K, Vaibhav K, Kumar A, Srikantaswamy R. Performance analysis of brain tumour image classification using CNN and SVM. In2020 Second International Conference on Inventive Research in Computing Applications (ICIRCA) 2020 Jul 15 (pp. 537-542). IEEE. doi: 10.1109/ICIRCA48905.2020.9183023
Lai Y. A comparison of traditional machine learning and deep learning in image recognition. InJournal of Physics: Conference Series 2019 Oct 1 (Vol. 1314, No. 1, p. 012148). IOP Publishing. doi: 10.1088/1742-6596/1314/1/012148.
Umri BK, Utami E, Kurniawan MP. Tinjauan literatur sistematik tentang deteksi covid-19 menggunakan convolutional neural networks. Creative Information Technology Journal. 2021 Mar 31;8(1):9-21. doi: 10.24076/citec.2021v8i1.261.
Tan M, Le Q. Efficientnet: Rethinking model scaling for convolutional neural networks. InInternational conference on machine learning 2019 May 24 (pp. 6105-6114). PMLR. doi: 10.2310/8000.2013.131108.
Sugiyono, Metode Penelitian Kuantitatif, Kualitatif, dan R&D, 27th ed. Bandung: ALFABETA, 2017. [Online]. Available from: https://cvalfabeta.com/product/metode-penelitian-kuantitatif-kualitatif-dan-rd-mpkk/
Polat H, Danaei Mehr H. Classification of pulmonary CT images by using hybrid 3D-deep convolutional neural network architecture. Applied Sciences. 2019 Mar 6;9(5):940. doi: 10.3390/app9050940.