Jaringan Syaraf Tiruan Perambatan Balik untuk Klasifikasi Covid-19 Berbasis Tekstur Menggunakan Orde Pertama Berdasarkan Citra Chest X-Ray
DOI:
https://doi.org/10.25126/jtiik.2022945663Abstrak
COVID-2019 pertama kali muncul di kota Wuhan, Cina pada Desember 2019, kemudian menyebar dengan cepat ke seluruh dunia dan menjadi pandemi. Pandemi COVID-19 telah menyebabkan dampak yang cukup fataluntukkesehatan masyaraka. Merupakan hal yang sangat penting untuk mendeteksi kasus positif sedini mungkin untuk pencegahan penyebaran lebih lanjut dari virus ini. Teknik tes paling umum yang saat ini digunakan untuk mendiagnosa COVID-19adalah reverse-transcriptase polymerase chain reaction (RT-PCR). Pencitraan radiologis dada seperti chest X-ray memiliki peran penting dalam diagnosis dinipenyakit ini. Karena sensitivitas RT-PCR rendah 60% -70%, bahkan jika hasil negatif diperoleh, gejala dapat dideteksi dengan pemeriksaan gambar radiologi pasien. Teknik kecerdasan buatanyang digabungkan dengan pencitraan radiologis dapat membantu untuk mendiagnosis COVID-19 dengan lebih cepat dan akurat.Proses klasifikasi pada penelitian ini terdapat beberapa tahapan yaitu pra-pengolahan, segmentasi, ekstraksi ciri, dan klasifikasi. Ekstraksi ciri yang digunakan adalah berdasarkan tekstur orde pertama dan klasifikasi yang digunakan adalah jaringan syaraf tiruan perambatan balik. Sistem klasifikasi pada penelitian ini menghasilkan rata-rata akurasi klasifikasi sebesar 94,17% untuk kelas normal dan 77,5% untuk COVID-19. Hasil akurasi tertinggi didapat pada skenario pertama dengan hasil akurasi sebesar 88,8%. Nilai rata-rata sensitivitas yang didapat pada penelitian ini sebesar 94,17% untuk kelas normal dan 76,67% untuk kelas COVID-19. Nilai rata-rata spesifisitas yang didapat pada penelitian ini sebesar 76,67% untuk kelas normal dan 94,17% untuk kelas COVID-19.
Abstract
Covid-2019 first appeared in Wuhan, China, in December 2019, then quickly spread throughout the world and became a pandemic. The Covid-19 pandemic has had a fatal impact on public health. It is crucial to detect positive cases as early as possible to prevent the further spread of this virus. The most common test technique currently used to diagnose Covid -19 is the reverse-transcriptase polymerase chain reaction (RT-PCR). Chest radiological imaging such as chest X-ray has a vital role in the early diagnosis of this disease. Due to the low RT-PCR sensitivity of 60%-70%, symptoms can be detected by examining the patient's radiological images even if a negative result is obtained. Artificial intelligence techniques combined with radiological imaging can help diagnose Covid -19 more quickly and accurately. The classification process in this study consists of several stages, namely pre-processing, segmentation, feature extraction, and classification. The feature extraction used is based on the first-order texture, and the classification used is a backpropagation neural network. The classification system in this study resulted in an average classification accuracy of 94.17% for the normal class and 77.5% for Covid -19. The highest accuracy results were obtained in the first scenario, with an accuracy of 88.8%. The average sensitivity value obtained in this study was 94.17% for the normal class and 76.67% for the Covid -19 class. The average specificity value obtained in this study was 76.67% for the normal class and 94.17% for the Covid -19 class.
Downloads
Referensi
ALQUDAH, A.M., QAZAN, S., ALQURAN, H., QASMIEH, I.A. AND ALQUDAH, A., 2020. Covid-2019 Detection Using X-Ray Images And Artificial Intelligence Hybrid Systems.
AMALIA, A., HERTIANA, B.D.K. AND SUHENDI, A., 2019. Image Processing on Wick Method Hydroponic Mustard Greens Leaf Growth with IoT Based Monitoring System. 6(2), pp.5289–5296.
BELEITES, C., SALZER, R. AND SERGO, V., 2013. Validation of soft classification models using partial class memberships: An extended concept of sensitivity & co. applied to grading of astrocytoma tissues. Chemometrics and Intelligent Laboratory Systems, 122, pp.12–22.
BERTALMIO, M., VESE, L., SAPIRO, G. AND OSHER, S., 2003. Image filling-in in a decomposition space. IEEE International Conference on Image Processing, 1, pp.853–855.
COHEN, J.P., 2020. covid-chest-xray-dataset. [online] Available at: <https://github.com/ieee8023/covid-chestxray-dataset> [Accessed 8 Jul. 2020].
DJALANTE, R., LASSA, J., SETIAMARGA, D., SUDJATMA, A., INDRAWAN, M., HARYANTO, B., MAHFUD, C., SINAPOY, M.S.,
DJALANTE, S., RAFLIANA, I., GUNAWAN, L.A., SURTIARI, G.A.K. AND WARSILAH, H., 2020. Review and analysis of current responses to COVID-19 in Indonesia: Period of January to March 2020. Progress in Disaster Science, 6, p.100091.
FAN, D.-P., ZHOU, T., JI, G.-P., ZHOU, Y., CHEN, G., FU, H., SHEN, J. AND SHAO, L., 2020. Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images. IEEE Transactions on Medical Imaging, XX(XX), pp.1–1.
GONG, S., LIU, C., JI, Y., ZHONG, B. AND LI, Y., 2019. Modeling and Optimization in Science and Technologies: Advanced Image and Video Processing Using MATLAB. Cham: Springer International Publishing AG,.
GONG, S., LIU, C., JI, Y., ZHONG, B., LI, Y. AND DONG, H., 2019. Advanced Image and Video Processing Using MATLAB (Modeling and Optimization in Science and Technologies). In: 1st ed. Cham: Springer International Publishing AG.
KANNE, J.P., LITTLE, B.P., CHUNG, J.H., ELICKER, B.M. AND KETAI, L.H., 2020. Essentials for radiologists on COVID-19: An update-radiology scientific expert panel. Radiology, 296(2), pp.E113–E114.
KARTIKA, D.S.Y. AND HERUMURTI, D., 2017. Koi fish classification based on HSV color space. Proceedings of 2016 International Conference on Information and Communication Technology and Systems, ICTS 2016, pp.96–100.
KARYATI, C.M., WIDIYANTO, S., MUSLIM, A. AND SUHATRIL, R.J., 2013. Analisis dan Pengolahan Citra Medis. Jakarta: Universitas Gunadarma.
KHOBRAGADE, S., TIWARI, A., PATIL, C.Y. AND NARKE, V., 2017. Automatic detection of major lung diseases using Chest Radiographs and classification by feed-forward artificial neural network. 1st IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems, ICPEICES 2016, pp.1–5.
KHOONG, W.H., 2020. COVID-19 Xray Dataset (Train & Test Sets) with COVID-19 CNN Pneumonia Detector. [online] Available at: <https://www.kaggle.com/khoongweihao/covid19-xray-dataset-train-test-sets/data> [Accessed 8 Jul. 2020].
KUSUMADEWI, S., 2003. Artificial Intelligent (Teknik dan Aplikasinya). Yogyakarta: Graha Ilmu.
KUSUMADEWI, S., 2004. Membangun Jaringan Syaraf Tiruan Menggunakan Matlab dan Excel Link. Yogyakarta: Graha Ilmu.
LI, K., FANG, Y., LI, W., PAN, C., QIN, P., ZHONG, Y., LIU, X., HUANG, M., LIAO, Y. AND LI, S., 2020. CT image visual quantitative evaluation and clinical classification of coronavirus disease (COVID-19). European Radiology.
LIANG, T., 2020. Handbook of COVID-19 Prevention and Treatment. Handbook of Covid-19, Prevention and Treatment, [online] p.68. Available at: <https://covid-19.alibabacloud.com>.
MASRANI, H., RUSLIANTO, I. AND ILHAMSYAH, 2018. Aplikasi Pengenalan Pola Pada Huruf Tulisan Tangan Menggunakan Jaringan Saraf Tiruan Dengan Metode Ekstraksi Fitur Geometri. Coding, Sistem Komputer Untan, [online] 06(02), pp.69–78. Available at: <http://jurnal.untan.ac.id/index.php/jcskommipa/article/view/26674>.
MBARKI, Z., SEDDIK, H. AND BEN BRAIEK, E., 2018. Non blind image restoration scheme combining parametric wiener filtering and BM3D denoising technique. 2018 4th International Conference on Advanced Technologies for Signal and Image Processing, ATSIP 2018, (3), pp.1–5.
MOONEY, P., 2018. Chest X-Ray Images (Pneumonia). [online] Available at: <https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia> [Accessed 8 Jul. 2020].
MUNIR, R., 2004. Pengolahan Citra Digital dengan Algoritmik. Bandung: Informatika.
OZTURK, T., TALO, M., YILDIRIM, E.A., BALOGLU, U.B., YILDIRIM, O. AND RAJENDRA ACHARYA, U., 2020. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Computers in Biology and Medicine, [online] 121(April), p.103792. Available at: <https://doi.org/10.1016/j.compbiomed.2020.103792>.
PERMATA, E., MUNARTO, R. AND INDRA, G.A.., 2016. Klasifikasi Glaukoma Menggunakan Neural Network Backpropagation. Prosiding SENTIA 2016, 8, pp.158–163.
PRIETO, Y. AND LINDQUIST, C.S., 1998. Locally adaptive orientation Wiener image filter with local noise estimate. Conference Record of the Asilomar Conference on Signals, Systems and Computers, 1, pp.338–342.
QIN, F., 2012. Blind image restoration based on Wiener filtering and defocus point spread function estimation. 2012 5th International Congress on Image and Signal Processing, CISP 2012, (Cisp), pp.360–363.
RAFIKASARI, A., 2020. Formulating Indonesia’s Covid-19 Policy based on South Korea’s Experience. Journal of Humanities and Education Development, 2(3), pp.170–176.
ROSNELLY, R., WAHYUNI, L. AND KUSANTI, J., 2018. Optimization of Region of Interest (ROI) Image of Malaria Parasites. Journal of Applied Intelligent System, 3(2), pp.87–95.
ROY, K., CHAUDHURY, S.S., BURMAN, M., GANGULY, A., DUTTA, C., BANIK, S. AND BANIK, R., 2019. A comparative study of lung cancer detection using supervised neural network. 2019 International Conference on Opto-Electronics and Applied Optics, Optronix 2019, pp.1–5.
SHEER, A.H. AND AL-ANI, A.A., 2019. The effect of regularization parameter within non-blind restoration algorithm using modified iterative wiener filter for medical image. Proceedings - 2018 1st Annual International Conference on Information and Sciences, AiCIS 2018, pp.77–81.
SUNOJ, S., SIVARAJAN, S., MAHARLOOEI, M., BAJWA, S.G., HARMON, J.P., NOWATZKI, J. AND IGATHINATHANE, C., 2017. Identification and counting of soybean aphids from digital images using shape classification. Transactions of the ASABE, 60(5), pp.1467–1477.
SWASTIKA, W., STUDI, P., INFORMATIKA, T. AND KORESPONDENSI, P., 2020. Studi Awal Deteksi Covid-19 Menggunakan Citra Ct Berbasis Deep Preliminary Study of Covid-19 Detection Using Ct Image Based on. Jurnal Teknologi Informasi dan Ilmu Komputer, 7(3), pp.629–634.
TAMMANA, G.A. AND ZHENG, Y.F., 2006. Image denoising using multi-resolution coefficient support based empirical Wiener filtering. Proceedings - International Conference on Image Processing, ICIP, (6), pp.2613–2616.
TENA, S., TENA, S. AND TENA, S., 2012. Image Enhancement Mengggunakan Metode Linear Filtering Dan Stationary Wavelet Transform. Majalah Ilmiah Teknik Elektro, 8(2).
WORLD HEALTH ORGANIZATION, 2021. WHO Coronavirus Disease (COVID-19) Dashboard. [online] Available at: <https://covid19.who.int/> [Accessed 15 Feb. 2021].
XIE, X., 2020. Chest CT for Typical 2019-nCoV Pneumonia: Relationship to Negative RT-PCR Testing. Journal of Clinical Microbiology, [online] 58(April), pp.1–5. Available at: <https://doi.org/10.1016/j.jcv.2020.104384%0Ahttp://dx.doi.org/10.1016/S2213-2600(20)30315-5%0Ahttps://doi.org/10.1016/j.rbmo.2020.06.001%0Ahttps://doi.org/10.1016/j.mayocp.2020.04.004%0Ahttps://doi.org/10.1016/j.cmi.2020.06.019%0Ahttps://doi.org/10.1080/1>.
YANG, L., ZHANG, X. AND REN, J., 2011. Adaptive Wiener filtering with Gaussian fitted point spread function in image restoration. ICSESS 2011 - Proceedings: 2011 IEEE 2nd International Conference on Software Engineering and Service Science, (2), pp.208–212.
YOHANNES, E., MAHMUDY, W.F. AND RAHMI, A., 2015. Penentuan Upah Minimum Kota Berdasarkan Tingkat Inflasi Menggunakan Backpropagation Neural Network (BPNN). Jurnal Teknologi Informasi dan Ilmu Komputer, 2(1), p.34.
ZOTIN, A., HAMAD, Y., SIMONOV, K. AND KURAKO, M., 2019. Lung boundary detection for chest X-ray images classification based on GLCM and probabilistic neural networks. Procedia Computer Science, [online] 159, pp.1439–1448. Available at: <https://doi.org/10.1016/j.procs.2019.09.314>.
Unduhan
Diterbitkan
Terbitan
Bagian
Lisensi
Artikel ini berlisensi Creative Common Attribution-ShareAlike 4.0 International (CC BY-SA 4.0)
Penulis yang menerbitkan di jurnal ini menyetujui ketentuan berikut:
- Penulis menyimpan hak cipta dan memberikan jurnal hak penerbitan pertama naskah secara simultan dengan lisensi di bawah Creative Common Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) yang mengizinkan orang lain untuk berbagi pekerjaan dengan sebuah pernyataan kepenulisan pekerjaan dan penerbitan awal di jurnal ini.
- Penulis bisa memasukkan ke dalam penyusunan kontraktual tambahan terpisah untuk distribusi non ekslusif versi kaya terbitan jurnal (contoh: mempostingnya ke repositori institusional atau menerbitkannya dalam sebuah buku), dengan pengakuan penerbitan awalnya di jurnal ini.
- Penulis diizinkan dan didorong untuk mem-posting karya mereka online (contoh: di repositori institusional atau di website mereka) sebelum dan selama proses penyerahan, karena dapat mengarahkan ke pertukaran produktif, seperti halnya sitiran yang lebih awal dan lebih hebat dari karya yang diterbitkan. (Lihat Efek Akses Terbuka).
