Pengaruh Teknik Pre-Processing Citra X-Ray terhadap Performa ResNet18 dalam Klasifikasi Penyakit Paru-Paru
DOI:
https://doi.org/10.29407/rh544w60Abstract
Kanker paru-paru merupakan penyakit dengan tingkat kematian tertinggi yang membutuhkan deteksi dini untuk meningkatkan peluang kesembuhan pasien. Penggunaan citra X-ray dada sering terkendala masalah kontras rendah dan noise. Penelitian ini bertujuan menganalisis pengaruh penerapan teknik pre-processing citra terhadap performa model ResNet18 dalam mengklasifikasikan kelainan paruparu menggunakan 3.475 data dari Kaggle. Tahapan pre-processing yang diterapkan meliputi grayscale conversion, resizing, CLAHE, normalization, dan Gaussian Filtering. Hasil penelitian menunjukkan bahwa penerapan pre-processing berhasil meningkatkan performa model secara konsisten, dengan nilai akurasi meningkat dari 85,0% menjadi 87,5%, presisi menjadi 90,2%, recall menjadi 85,3%, dan F1-score menjadi 87,6%. Model juga menunjukkan tingkat kepercayaan prediksi mencapai 99,01% dengan waktu pemrosesan efisien sebesar 1,391 detik. Kesimpulannya, pengondisian data masukan terbukti secara signifikan mengoptimalkan ekstraksi fitur visual model sehingga sangat potensial diimplementasikan sebagai alat bantu triase medis yang cepat dan akurat.
Keywords:
Citra X-ray, Deep Learning, Kanker Paru-Paru, ResNet18##plugins.themes.default.displayStats.downloads##
References
[1] J. A. Barta, C. A. Powell, and J. P. Wisnivesky, “Global epidemiology of lung cancer,”
Annals of Global Health, vol. 85, no. 1, 2019, doi: 10.5334/aogh.2419.
[2] I. M. Baltruschat, H. Nickisch, M. Grass, T. Knopp, and A. Saalbach, “Comparison of deep
learning approaches for multi-label chest X-ray classification,” Scientific Reports, vol. 9,
no. 1, 2019, doi: 10.1038/s41598-019-42294-8.
[3] K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition
(CVPR), 2016, pp. 770–778, doi: 10.1109/CVPR.2016.90.
[4] A. Giełczyk, A. Marciniak, and M. Tarczewska, “Pre-processing methods in chest X-ray
image classification,” PLOS ONE, vol. 17, no. 4, 2022, doi:
10.1371/journal.pone.0265949.
[5] T. Rahman, A. Khandakar, Y. Qiblawey, et al., “Exploring the effect of image enhancement
techniques on COVID-19 detection using chest X-ray images,” Computers in Biology and
Medicine, vol. 132, 2021, Art. no. 104319, doi: 10.1016/j.compbiomed.2021.104319.
[6] A. Ait Nasser and M. A. Akhloufi, “A review of recent advances in deep learning models
for chest disease detection using radiography,” Diagnostics, vol. 13, no. 1, 2023, doi:
10.3390/diagnostics13010159.
[7] R. Yamashita, M. Nishio, R. K. G. Do, and K. Togashi, “Convolutional neural networks:
an overview and application in radiology,” Insights into Imaging, vol. 9, pp. 611–629,
2018, doi: 10.1007/s13244-018-0639-9.
[8] M. E. H. Chowdhury, T. Rahman, A. Khandakar, et al., “Can AI help in screening viral and
COVID-19 pneumonia?” IEEE Access, vol. 8, pp. 132665–132676, 2020, doi:
10.1109/ACCESS.2020.3010287.
[9] A. E. W. Johnson, T. J. Pollard, S. J. Berkowitz, et al., “MIMIC-CXR, a de-identified
publicly available database of chest radiographs with free-text reports,” Scientific Data,
vol. 6, no. 1, 2019, Art. no. 317, doi: 10.1038/s41597-019-0322-0.
[10] C. Shorten and T. M. Khoshgoftaar, “A survey on image data augmentation for deep
learning,” Journal of Big Data, vol. 6, no. 1, 2019, doi: 10.1186/s40537-019-0197-0.
[11] F. Hussein, S. H. Ali, and B. A. Mohammed, “Hybrid CLAHE-CNN deep neural networks
for classifying lung diseases from X-ray images,” Electronics, vol. 11, no. 19, 2022, doi:
10.3390/electronics11193075.
[12] P. Chlap, H. Min, N. Vandenberg, J. Dowling, L. Holloway, and A. Haworth, “A review of
medical image data augmentation techniques for deep learning applications,” Journal of
Medical Imaging and Radiation Oncology, vol. 65, no. 5, pp. 545–563, 2021, doi:
10.1111/1754-9485.13261.
[13] D. S. Kermany, M. Goldbaum, W. Cai, et al., “Identifying medical diagnoses and treatable
diseases by image-based deep learning,” Cell, vol. 172, no. 5, pp. 1122–1131.e9, 2018,
doi: 10.1016/j.cell.2018.02.010.
[14] S. Showkat and S. Qureshi, “Efficacy of transfer learning-based ResNet models in chest
X-ray image classification for detecting COVID-19 pneumonia,” Chemometrics and
Intelligent Laboratory Systems, vol. 224, 2022, Art. no. 104534, doi:
10.1016/j.chemolab.2022.104534.
[15] M. Buda, A. Maki, and M. A. Mazurowski, “A systematic study of the class imbalance
problem in convolutional neural networks,” Neural Networks, vol. 106, pp. 249–259,
2018, doi: 10.1016/j.neunet.2018.07.011.
[16] A. Tharwat, “Classification assessment methods,” Applied Computing and Informatics,
vol. 17, no. 1, pp. 168–192, 2021, doi: 10.1016/j.aci.2018.08.003.
[17] R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-CAM:
Visual explanations from deep networks via gradient-based localization,” International
Journal of Computer Vision, vol. 128, pp. 336–359, 2020, doi: 10.1007/s11263-019-
01228-7.
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