Purwarupa Sistem Deteksi COVID-19 Berbasis Website Menggunakan Algoritma Convolutional Neural Network
COVID-19 is a disease caused by coronavirus 2 (SARS-CoV-2). This virus belongs to the group of viruses that infect the respiratory system. Furthermore, the rapid rate of spread has made several countries implement a policy of implementing a lockdown to prevent the spread of this virus. In Indonesia, the government implemented the policy of "Pemberlakuan Pembatasan Kegiatan Masyarakat (PPKM)" to suppress the spread of this virus. Based on data from the Task Force for the Acceleration of Handling COVID-19 of the Republic of Indonesia, the number of confirmed positive cases as of August 6, 2021 is 3,568,331 people with a death toll of 102,375. The existence of the COVID-19 vaccine is currently under threat, this is due to the emergence of new variants of the COVID-19 virus. The RT-PCR method as the main standard used throughout the world in detecting this virus has a fairly high specificity, which is around 95 percent, which is a manual process that can only be done by health workers. In addition, this test takes a long time and the number of testing facilities is limited. The presence of X-ray scanning machines in hospitals can be used to increase the availability of COVID-19 testing facilities. The thoracic x-ray image generated by the scanner can be used to detect the virus easily, quickly and precisely. In this study, a website-based system was designed to detect the COVID-19 virus in thoracic x-ray images using a convolutional neural network algorithm. The results obtained show that this system is able to classify chest x-ray images into three classes, namely COVID-19, Viral Pneumonia, and normal. The accuracy value obtained is 89.6% and the F1 value is 87.9%.
 Wang, L.; Wong, A. Covid-net: A tailored deep convolutional neural network design for detection of covid-19 cases from chest
X-ray images. arXiv 2020, arXiv:2003.09871. [CrossRef] [PubMed]
 Afzal, A. Molecular diagnostic technologies for COVID-19: Limitations and challenges. J. Adv. Res. 2020. [CrossRef] [PubMed]
 Chen, H., Ai, L., Lu, H., & Li, H. Clinical and imaging features of COVID-19.Radiology of Infectious Diseases, April, 1–8. 2020
 Cherian, T.; Mulholland, E.K.; Carlin, J.B.; Ostensen, H.; Amin, R.; Campo, M.D.; Greenberg, D.; Lagos, R.; Lucero, M.; Madhi,
S.A.; et al. Standardized interpretation of paediatric chest radiographs for the diagnosis of pneumonia in epidemiological studies. Bull. World Health Organ 2005, 83, 353–359.
 Franquet, T. Imaging of pneumonia: Trends and algorithms. Eur. Respir. J. 2001, 18, 196–208. [CrossRef] [PubMed]
 Davies, H.E.; Wathen, C.G.; Gleeson, F.V. The risks of radiation exposure related to diagnostic imaging and how to minimise
them. BMJ 2011, 342. [CrossRef]
 Jaiswal AK, Tiwari P, Kumar S, Gupta D, Khanna A, and Rodrigues JJ. Identifying pneumonia in chest X-rays: A deep learning approach. Measurement, 145:511-518, 2019.
 Antin B, Kravitz J, and Martayan E. Detecting Pneumonia in Chest X-Rays with Supervised Learning. http://cs229.stanford.edu/proj2017/final-reports/5231221.pdf, 1-5, 2017.
 Das NN, Kumar N, Kaur M, Kumar V, and Singh D. Automated Deep Transfer LearningBased Approach for Detection of COVID-19 Infection in Chest X-rays. IRBM, https://doi.org/10.1016/j.irbm.2020.07.001, 2020.
 Gaál G, Maga B, and Lukács A. Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation. arXiv:2003.10304, 2020.
 Pereira, R. M., Bertolini, D., Teixeira, L. O., Silla, C. N., & Costa, Y. M. G. (2020). COVID-19 identification in chest X-ray images on flat
and hierarchical classification scenarios. Computer Methods and Programs in Biomedicine, 105532
 Öztürk, Ş., & Akdemir, B. Comparison of Edge Detection Algorithms for Texture Analysis on Glass Production. Procedia - Social and Behavioral Sciences, 195(July), 2675–2682. 2015
 Ribbens, A., Hermans, J., Maes, F., Vandermeulen, D. & Suetens, P. Unsupervised segmentation, clustering, and groupwise
registration of heterogeneous populations of brain mr images. IEEE transactions on medical imaging 33, 201–224. 2013.
 Gong, M., Liang, Y., Shi, J., Ma, W. & Ma, J. Fuzzy c-means clustering with local information and kernel metric for image
segmentation. IEEE transactions on image processing 22, 573–584. 2012.
 Kuo, J.-w. et al. Nested graph cut for automatic segmentation of high-frequency ultrasound images of the mouse embryo.
IEEE transactions on medical imaging 35, 427–441. 2015.
 Li, G. et al. Automatic liver segmentation based on shape constraints and deformable graph cut in ct images. IEEE
Transactions on Image Process. 24, 5315–5329. 2015.
 He, K., Gkioxari, G., Dollár, P. & Girshick, R. Mask r-cnn. In Proceedings of the IEEE international conference on
computer vision, 2961–2969. 2017.
 Ronneberger, O., Fischer, P. & Brox, T. U-net: Convolutional networks for biomedical image segmentation. In International
Conference on Medical image computing and computer-assisted intervention, 234–241 (Springer, 2015).
 Zhang, K., Zhang, L., Lam, K.-M. & Zhang, D. A level set approach to image segmentation with intensity inhomogeneity.
IEEE transactions on cybernetics 46, 546–557. 2015.
 Ding, K. & Xiao, L. A simple method to improve initialization robustness for active contours driven by local region fitting
energy. arXiv preprint arXiv:1802.10437. 2018.
 Kumar I., Rawat J. and Bhadauria H. S., "A Conventional Study of Edge Detection Technique in Digital Image Processing," Int. J. Comput. Sci. Mob. Comput., vol. 3, no. 4, pp. 328–334, 2014.
 Rashmi, M. Kumar, and Saxena R., "Algorithm and Technique on Various Edge Detection: A Survey," Int. J. Signal Image Process., vol. 4, no. 3, pp. 65–75, 2013.
 Makandar A. and Halalli B., “Image Enhancement Techniques using Highpass and Lowpass Filters,” Int. J. Comput. Appl., vol. 109, no. 14, pp. 12–15, 2015.
 Canny J., “A Computational Approach to Edge Detection,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 8, no. 6, p. pp.679-698, 1986.
 Lahani J., Bade A., Sulaiman H. A. and Muniandy R. K., “InTEC : Integration of Enhanced Entropy – Canny Technique for Edge Detection in Digital X-Ray Images,” ASM Sci. J., vol. 11, no. 3, pp. 161–167, 2018.
 Shi, F.; Wang, J.; Shi, J.; Wu, Z.; Wang, Q.; Tang, Z.; He, K.; Shi, Y.; Shen, D. Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for covid-19. IEEE Rev. Biomed. Eng. 2020. [CrossRef]
 N.-A-A.; Ahsan, M.; Based, M.A.; Haider, J.; Kowalski, M.
COVID-19 Detection from Chest X-ray Images Using Feature Fusion and Deep Learning. Sensors 2021, 21,
 Ahammed, K.; Satu, M.S.; Abedin, M.Z.; Rahaman, M.A.; Islam, S.M.S. Early Detection of Coronavirus Cases Using Chest X-ray
Images Employing Machine Learning and Deep Learning Approaches. medRxiv 2020. medRxiv 2020.06.07.20124594.
 Chowdhury, N.K.; Rahman, M.M.; Kabir, M.A. PDCOVIDNet: A parallel-dilated convolutional neural network architecture for
detecting COVID-19 from chest X-ray images. Health Inf. Sci. Syst. 2020, 8, 1–14. [CrossRef] [PubMed]
 Abbas, A.; Abdelsamea, M.M.; Gaber, M.M. Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network. Appl. Intell. 2021, 51, 854–864. [CrossRef]
 Wang, N.; Liu, H.; Xu, C. Deep Learning for The Detection of COVID-19 Using Transfer Learning and Model Integration. In Proceedings of the 2020 IEEE 10th International Conference on Electronics Information and Emergency Communication (ICEIEC), Beijing, China, 17–19 July 2020; pp. 281–284.
 Che Azemin, M.Z.; Hassan, R.; Mohd Tamrin, M.I.; Md Ali, M.A. COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings. Int. J. Biomed. Imaging 2020. [CrossRef]
 Khan, I.U.; Aslam, N. A Deep-Learning-Based Framework for Automated Diagnosis of COVID-19 Using X-ray Images. Information 2020, 11, 419. [CrossRef]
 Eleanor Bird. Tests may miss more than 1 in 5 COVID-19 cases. Retrieved from https://www.medicalnewstoday.com/articles/ tests-may-miss-more-than-1-in-5-covid-19-cases, 2020
 Emily Waltz. April 2020. Testing the tests: Which COVID-19 tests are most accurate? Retrieved from https://spectrum.ieee.org/thehuman-os/biomedical/diagnostics/testing-tests-which-covid19-tests-are-most-accurate
 Narin, A., Kaya, C., & Pamuk, Z. (2020). Automatic Detection of Coronavirus Disease (COVID-19)
Using X-ray Images and Deep Convolutional Neural Networks. arXiv preprint
 Paul Mooney, Chest X-Ray Images (Pneumonia), Data Sets. Available online:
https://www.kaggle.com/nabeelsajid917/covid-19-x-ray-10000-images (accessed on 10 May 2021).
 Tawsifur Rahman, COVID-19 Radiography Database, Data Sets. Available online: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database (accessed on 10 May 2021).
 Yoo, S.H.; Geng, H.; Chiu, T.L.; Yu, S.K.; Cho, D.C.; Heo, J.; Choi, M.S.; Choi, I.H.; Van, C.C.; Nhung, N.V. Deep Learning-Based Decision-Tree Classifier for COVID-19 Diagnosis From Chest X-ray Imaging. Front. Med. 2020, 7, 427. [CrossRef] [PubMed]
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