Penerapan Convolutional Neural Network pada Citra Rontgen Paru-Paru untuk Deteksi SARS-CoV-2
Abstract
COVID-19 was officially declared as a pandemic by the WHO on March 11, 2020. For COVID-19, the testing methods commonly used are the Antibody Testing and RT-PCR Testing. Both methods are considered to be the most effective in determining whether a person has been suffered from COVID-19 or not. However, alternative testing methods need to be tried. One of them is using the Convolutional Neural Network. This study aims to measure the performance of CNN in classifying x-ray image of a person’s chest to determine whether the person is suffered from COVID-19 or not. The CNN model that was built consists of 1 convolutional 2D layer, 2 activation layers, 1 maxpooling layer, 1 dropout layer, 1 flatten layer, and 1 dense layer. Meanwhile, the chest x-ray image dataset used is the COVID-19 Radiography Database. This dataset consists of 3 classes, i.e. COVID-19 class, NORMAL class, and VIRAL_PNEUMONIA. The experiments consisted of 4 scenarios and were carried out using Google Colab. Based on the experiments, the CNN model can achieve an accuracy of 98.69%, a sensitivity of 97.71%, and a specificity of 98.90%. Thus, CNN has a very good performance to classify the disease based on a person’s chest x-ray.
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References
R. Sebayang, “WHO Nyatakan Wabah COVID-19 jadi Pandemi, Apa Maksudnya?,” 2020. https://www.cnbcindonesia.com/news/20200312075307-4-144247/who-nyatakan-wabah-covid-19-jadi-pandemi-apa-maksudnya (diakses Mar 25, 2020).
W. H. Organization, “T3: Test. Treat. Track. Scaling up diagnostic testing, treatment and surveillance for malaria,” 2012. https://www.who.int/malaria/publications/atoz/t3_brochure/en/ (diakses Mar 22, 2020).
R. H. Permana, “Strategi Korea Selatan Pukul Mundur Corona: Lacak, Uji, Obati!,” 2020. https://news.detik.com/berita/d-4946883/strategi-korea-selatan-pukul-mundur-corona-lacak-uji-obati (diakses Mar 25, 2020).
T. administrator situs KawalCOVID19.id, “Rapid test atau swab test: Apa bedanya? Mana yang lebih baik?,” 2020. https://kawalcovid19.id/content/1183/rapid-test-atau-swab-test-apa-bedanya-mana-yang-lebih-baik (diakses Apr 15, 2020).
A. Herráez, “Rapid tests for coronavirus: how do they work?,” 2020. https://network.febs.org/posts/64073-rapid-tests-for-coronavirus-how-do-they-work (diakses Mar 27, 2020).
L. Ying et al., “Diagnostic Indexes of a Rapid IgG/IgM Combined Antibody Test for SARS-CoV-2,” ivmedRx, 2020, doi: https://doi.org/10.1101/2020.03.26.20044883.
W. J. Palmer, “ACR Releases CT and Chest X-ray Guidance Amid COVID-19 Pandemic,” 2020. https://www.diagnosticimaging.com/view/acr-releases-ct-and-chest-x-ray-guidance-amid-covid-19-pandemic (diakses Mar 22, 2020).
W. Rawat dan Z. Wang, “Deep Convolutional Neural Networks for Image Classification : A Comprehensive Review,” Neural Comput. 29, hal. 2352–2449, 2017, doi: 10.1162/NECO_a_00990.
C. Liu et al., “TX-CNN: Detecting Tuberculosis in Chest X-ray Images using Convolutional Neural Network,” in Proceedings - International Conference on Image Processing, ICIP, 2017, hal. 2314–2318. doi: 10.1109/ICIP.2017.8296695.
S. U. K. Bukhari, S. S. K. Bukhari, A. Syed, dan S. S. H. Shah, “The Diagnostic Evaluation of Convolutional Neural Network (CNN) for The Assessment of Chest X-ray of Patients Infected with COVID-19,” MedRxiv, 2020, doi: https://doi.org/10.1101/2020.03.26.20044610.
I. Gogul dan V. S. Kumar, “Flower Species Recognition System Using Convolution Neural Networks and Transfer Learning,” 2017. doi: 10.1109/ICSCN.2017.8085675.
B. P. Hartato, T. Astuti, I. Pratika, R. Wahyudi, I. Santiko, dan A. D. Riyanto, “Artificial Neural Network Utilization for Analyzing Sentiment Polarity in Electronics Product Reviews,” in 3rd International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), 2018, hal. 209–214. doi: 10.1109/ICITISEE.2018.8720987.
M. Arfan, Ahmad Nurjalal, Maman Somantri, dan Sudjadi, “Pengenalan Aktivitas Manusia pada Area Tambak Udang dengan Convolutional Neural Network,” J. RESTI (Rekayasa Sist. dan Teknol. Informasi), vol. 5, no. 1, hal. 174–179, 2021, doi: 10.29207/resti.v5i1.2888.
N. Aloysius dan G. M, “A Review on Deep Convolutional Neural Networks,” in International Conference on Communication and Signal Processing (ICCSP), 2017, hal. 588–592. doi: 10.1109/ICCSP.2017.8286426.
T. Rahman, M. Chowdhury, dan A. Khandakar, “COVID-19 Radiography Database.” 2020. [Daring]. Tersedia pada: https://www.kaggle.com/tawsifurrahman/covid19-radiography-database
M. E. H. Chowdhury et al., “Can AI Help in Screening Viral and COVID-19 Pneumonia ?,” IEEE Access, vol. 8, hal. 132665–132676, 2020, doi: 10.1109/ACCESS.2020.3010287.
S. Sharma, “Activation Functions in Neural Networks,” 2017. https://towardsdatascience.com/activation-functions-neural-networks-1cbd9f8d91d6 (diakses Nov 20, 2020).
R. R. Rao dan K. Makkithaya, “Learning from a Class Imbalanced Public Health Dataset : a Cost-based Comparison of Classifier Performance,” Int. J. Electr. Eng., vol. 7, no. 4, hal. 2215–2222, 2017, doi: 10.11591/ijece.v7i4.pp2215-2222.
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