Klasifikasi COVID 19 dengan Metode EfficientNet berdasarkan CT scan Paru-paru

Akhmad Irsyad, Islamiyah Islamiyah, Hario Jati Setyadi, Fakhmul Amal

Abstract


Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) adalah virus penyebab Covid-19. Covid-19 adalah virus mematikan yang oleh Organisasi Kesehatan Dunia (WHO) ditetapkan sebagai pandemi karena penyebarannya yang cepat. Dua metode yang kini paling sering digunakan untuk mendeteksi Covid-19 adalah Rapid Diagnostic Test (RDT) dan Real-Time Reverse Transcription Polymerase Chain Reaction (RT-PCR). Menemukan strategi baru yang cepat dan tepat sangat penting karena kedua strategi memiliki kelebihan dan kekurangan. Penggunaan CT scan untuk menemukan Covid-19 adalah salah satu metode yang direkomendasikan. Makalah ini merekomendasikan identifikasi Covid-19 pada gambar CT menggunakan EfficientNet B0. tampil lebih unggul dari model tanpa CLAHE. Untuk performa EfficientNet B0 dengan CLAHE, akurasi, F-measure, recall, dan precision adalah 91,95%, 92,06%, 92,43%, dan 91,69%.


Keywords


Covid-19; Clasification; Deep Learning; EfficientNet

Full Text:

PDF

References


covid19.go.id, “Situasi COVID-19 di Indonesia,” , 2023. https://covid19.go.id/artikel/2023/06/02/situasi-covid-19-di-indonesia-update-2-juni-2023 (accessed Jun. 03, 2023).

M. Jenkins, O. Johnson, T. Helliwell and C. P. Johnson, “Case Report: Suspected COVID-19 death in the community - histological lung findings and the challenges faced by the pathologist,” , F1000Research, vol. 9, 2020, doi: 10.12688/f1000research.23629.1.

A. Ghaffari, R. Meurant and A. Ardakani, “COVID-19 Serological Tests: How Well Do They Actually Perform?,” , Diagnostics, vol. 10, no. 7, Jul. 2020, doi: 10.3390/diagnostics10070453.

A. Scohy, A. Anantharajah, M. Bodéus, B. Kabamba-Mukadi, A. Verroken and H. Rodriguez-Villalobos, “Low performance of rapid antigen detection test as frontline testing for COVID-19 diagnosis,” , Journal of Clinical Virology, vol. 129, Aug. 2020, doi: 10.1016/j.jcv.2020.104455.

X. Yang, Z. Jinyu, Z. Yichen, Z. Shanghang and P. Xie, “COVID-CT-Dataset: A CT Image Dataset about COVID-19,” , 2020. Accessed: Jul. 09, 2020. [Online]. Available: https://www.medrxiv.org/

Q. Yang … G. Wang, “Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss,” , IEEE Transactions on Medical Imaging, vol. 37, no. 6, pp. 1348–1357, Jun. 2018, doi: 10.1109/TMI.2018.2827462.

V. Kumar Singh, M. Abdel-Nasser, N. Pandey and D. Puig, “LungINFseg: Segmenting COVID-19 Infected Regions in Lung CT Images Based on a Receptive-Field-Aware Deep Learning Framework,” , Diagnostics, vol. 11, no. 2, p. 158, 2021, doi: 10.3390/diagnostics11020158.

Y. Wang and Q. Zeng, “Ovarian Tumor Texture Classification Based on Sparse Auto-Encoder Network Combined with Multi-Feature Fusion and Random Forest in Ultrasound Image,” , Journal of Medical Imaging and Health Informatics, vol. 11, no. 2, pp. 424–431, Oct. 2020, doi: 10.1166/JMIHI.2021.3298.

Z. He … W. K. Ming, “The Influence of Average Temperature and Relative Humidity on New Cases of COVID-19: Time-Series Analysis,” , JMIR Public Health Surveill 2021;7(1):e20495 https://publichealth.jmir.org/2021/1/e20495, vol. 7, no. 1, p. e20495, Jan. 2021, doi: 10.2196/20495.

E. A. A. Alaoui, S. C. K. Tekouabou, S. Hartini, Z. Rustam, H. Silkan and S. Agoujil, “Improvement in automated diagnosis of soft tissues tumors using machine learning,” , Big Data Mining and Analytics, vol. 4, no. 1, pp. 33–46, Mar. 2021, doi: 10.26599/BDMA.2020.9020023.

J. Shuja, E. Alanazi, W. Alasmary and A. Alashaikh, “Covid-19 Datasets: A Survey And Future Challenges,” , 2020, doi: 10.1101/2020.05.19.20107532.

I. D. Apostolopoulos and T. A. Mpesiana, “Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks,” , Physical and Engineering Sciences in Medicine, vol. 43, pp. 635–640, 2020, doi: 10.1007/s13246-020-00865-4.

M. Tan and Q. V Le, “EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks,” , in Proceedings of the 36th International Conference on Machine Learning, May 2019, pp. 6105–6114. Accessed: Jun. 08, 2021. [Online]. Available: http://proceedings.mlr.press/v97/tan19a.html

B. Baheti, S. Innani, S. Gajre and S. Talbar, “Eff-UNet: A novel architecture for semantic segmentation in unstructured environment,” , in IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2020, vol. 2020-June, pp. 1473–1481. doi: 10.1109/CVPRW50498.2020.00187.

E. Soares, P. Angelov, S. Biaso, M. Higa Froes and D. K. Abe, “SARS-CoV-2 CT-scan dataset: A large dataset of real patients CT scans for SARS-CoV-2 identification,” , medrxiv, 2020, doi: 10.1101/2020.04.24.20078584.

R. E. Putra, H. Tjandrasa and N. Suciati, “Severity Classification of Non-Proliferative Diabetic Retinopathy Using Convolutional Support Vector Machine,” , International Journal of Intelligent Engineering and Systems, vol. 13, no. 4, 2020, doi: 10.22266/ijies2020.0831.14.

J. Shijie, W. Ping, J. Peiyi and H. Siping, “Research on data augmentation for image classification based on convolution neural networks,” , Proceedings - 2017 Chinese Automation Congress, CAC 2017, vol. 2017-January, pp. 4165–4170, Dec. 2017, doi: 10.1109/CAC.2017.8243510.

D. M. W. Powers, “Evaluation: From Precision, Recall And F-Measure to ROC, Informedness, Markedness & Correlation,” , ArXiv, vol. 2, no. 1, pp. 37–63, 2011, Accessed: May 14, 2019. [Online]. Available: http://www.bioinfo.in/contents.php?id=51

S. H. Park, J. M. Goo and C. H. Jo, “Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists,” , Korean Journal of Radiology, vol. 5, no. 1, p. 11, 2004, doi: 10.3348/KJR.2004.5.1.11.

A. Irsyad and H. Tjandrasa, “Detection of COVID-19 from Chest CT Images Using Deep Transfer Learning,” , International Conference On Information & Communication Technology And System (ICTS), 2021.




DOI: http://dx.doi.org/10.30872/jsakti.v5i2.14363

Refbacks

  • There are currently no refbacks.


Copyright (c) 2023 Sains, Aplikasi, Komputasi dan Teknologi Informasi

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

 

2nd Floor, Faculty of Computer Science and Information Technology
Jl. Panajam Kampus Gn. Kelua Universitas Mulawarman Samarinda-Kalimantan Timur 75123
Phone: +62 813 31112002 (Haviluddin) +62 811 8207777 (Reza)
E-Mail: jurnal.sakti.fkti@gmail.com; sakti@unmul.ac.id

Creative Commons License
Sains, Aplikasi, Komputasi dan Teknologi Informasi by http://e-journals.unmul.ac.id/index.php/jsakti eISSN: 2684-8473 is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.