Analisis Pengaruh Image Enhancement Pada Pendeteksian COVID-19 Berbasis Citra X-Ray

Authors

DOI:

https://doi.org/10.33633/tc.v22i1.7195

Keywords:

image enhancement, covid19, citra x-ray, CNN, SVM, KNN

Abstract

Penyakit virus corona 2019 (COVID-19) merupakan keadaan darurat kesehatan masyarakat secara global. Salah satu cara untuk dapat mendeteksi adanya COVID-19 adalah dengan memanfaatkan citra x-ray dada yang mengidentifikasi anomali pada area paru-paru. Namun terkadang citra yang didapatkan pada melalui scan x-ray memiliki kualitas yang buruk sehingga sulit secara langsung untuk bisa dianalisis secara manual atau menggunakan model machine learning. Untuk menghasilkan analisis yang lebih baik, biasanya citra akan ditingkatkan terlebih dahulu kualitasnya dengan teknik image enhancement. Banyak metode image enhancement yang bisa dimanfaatkan untuk keperluan ini yang tentunya akan memberikan pengaruh yang berbeda-beda pada hasil pendeteksian COVID-19. Penelitian ini bertujuan untuk menganalisis pengaruh metode-metode image enhancement pada pendeteksian COVID-19 melalui citra x-ray. Metode image enhancement yang dianalisis adalah log transformation, constrast stretching, histogram equalization (HE), dan contrast limited adaptive histogram equalization (CLAHE). Model pendeteksian COVID-19 akan berdasarkan pada beberapa model machine learning antara lain convolutional neural network (CNN), k-nearest neighbor (KNN) dan support vector machine (SVM). Dari eksperiment didapatkan bahwa pengaplikasian image enhancement HE dan CLAHE memberikan peningkatan akurasi pada pendeteksian COVID-19 khususnya ketika diterapkan pada model CNN yaitu sebesar 1,13% dan 2,25%.

References

Pillalamarry Mahesh, Yakkala Gnana Prathyusha, Botlagunta Sahithi and S Nagendram, "Covid-19 Detection from Chest X-Ray using Convolution Neural Networks", Journal of Physics: Conference Series, Volume 1804, International Conference of Modern Applications on Information and Communication Technology (ICMAICT) 22-23 October 2020, University of Babylon, Babylon-Hilla City, Iraq, 2021.

Civit-Masot J., Luna-Perejón F., Morales M.D., Civit A. Deep learning system for COVID-19 diagnosis aid using X-ray pulmonary images Applied Sciences (Switzerland), 10 (13) (2020), 10.3390/app10134640

Ozturk T., Talo M., Yildirim E.A., Baloglu U.B., Yildirim O., Rajendra Acharya U. Automated detection of COVID-19 cases using deep neural networks with X-ray images Computers in Biology and Medicine, 121 (April) (2020), Article 103792, 10.1016/j.compbiomed.2020.103792

Yoo S.H., Geng H., Chiu T.L., Yu S.K., Cho D.C., Heo J., Choi M.S., Choi I.H., Cung Van C., Nhung N.V., Min B.J., Lee H. Deep learning-based decision-tree classifier for COVID-19 diagnosis from chest X-ray imaging Frontiers in Medicine, 7 (July) (2020), pp. 1-8, 10.3389/fmed.2020.00427

Sethy P.K., Behera S.K., Ratha P.K., Biswas P. Detection of coronavirus disease (COVID-19) based on deep features and support vector machine International Journal of Mathematical, Engineering and Management Sciences, 5 (4) (2020), pp. 643-651, 10.33889/IJMEMS.2020.5.4.052

Minaee S., Kafieh R., Sonka M., Yazdani S., Jamalipour Soufi G. Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning Medical Image Analysis, 65 (2020), 10.1016/j.media.2020.101794 arXiv:2004.09363.

Panwar H., Gupta P.K., Siddiqui M.K., Morales-Menendez R., Singh V. Application of deep learning for fast detection of COVID-19 in X-Rays using nCOVnet.

Apostolopoulos I.D., Mpesiana T.A. Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks Physical and Engineering Sciences in Medicine, 43 (2) (2020), pp. 635-640, 10.1007/s13246-020-00865-4

Jain G., Mittal D., Thakur D., Mittal M.K. A deep learning approach to detect Covid-19 coronavirus with X-Ray images Biocybernetics and Biomedical Engineering, 40 (4) (2020), pp. 1391-1405, 10.1016/j.bbe.2020.08.008

Khan A.I., Shah J.L., Bhat M.M. CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images Computer Methods and Programs in Biomedicine, 196 (2020), Article 105581, 10.1016/j.cmpb.2020.105581 arXiv:2004.04931

Joseph Paul Cohen and Paul Morrison and Lan Dao COVID-19 image data collection, arXiv:2003.11597, 2020 https://github.com/ieee8023/covid-chestxray-dataset.

Zhuang, Liyun, and Yepeng Guan. 2019. "Image Enhancement Using Modified Histogram and Log-Exp Transformation" Symmetry 11, no. 8: 1062. https://doi.org/10.3390/sym11081062

Rafael C. González, Richard Eugene Woods (2007). Digital Image Processing. Prentice Hall. p. 85. ISBN 978-0-13-168728-8.

Hum, Yan Chai; Lai, Khin Wee; Mohamad Salim, Maheza Irna (October 11, 2014). "Multiobjectives bihistogram equalization for image contrast enhancement". Complexity. 20 (2): 22–36. Bibcode:2014Cmplx..20b..22H. doi:10.1002/cplx.21499.

S. M. Pizer, R. E. Johnston, J. P. Ericksen, B. C. Yankaskas and K. E. Muller, "Contrast-limited adaptive histogram equalization: speed and effectiveness," [1990] Proceedings of the First Conference on Visualization in Biomedical Computing, 1990, pp. 337-345, doi: 10.1109/VBC.1990.109340.

N. Dalal and B. Triggs, "Histograms of oriented gradients for human detection," 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), 2005, pp. 886-893 vol. 1, doi: 10.1109/CVPR.2005.177.

P. Mohanaiah, P. Sathyanarayana, L. GuruKumar, Image Texture Feature Extraction Using GLCM Approach, International Journal of Scientific and Research Publications, Volume 3, Issue 5, May 2013

Cortes, Corinna; Vapnik, Vladimir (1995). "Support-vector networks" (PDF). Machine Learning. 20 (3): 273–297. CiteSeerX 10.1.1.15.9362. doi:10.1007/BF00994018. S2CID 206787478.

Wahyono, Peningkatan Kecepatan Algoritma K-NN Untuk Sistem Pengklasifikasian Kendaraan Bermotor, Jurnal Teknologi Informasi, Vol. 19, No. 2, pp. 190-196, 2020.

Karen Simonyan, Andrew Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, 4 Sep 2014, https://arxiv.org/abs/1409.1556

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Published

2024-06-21

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Vol. 22 No. 1 (2023): Februari 2023

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