Lung X-ray Image Similarity Analysis Using RGB Pixel Comparison Method

  • Sofyan Pariyasto Sekolah Tinggi Ilmu Kesehatan Mitra Sejati
  • Suryani . Sekolah Tinggi Ilmu Kesehatan Mitra Sejati
  • Vicky Arfeni Warongan Sekolah Tinggi Ilmu Kesehatan Mitra Sejati
  • Arini Vika Sari Universitas Budi Darma
  • Wahyu Wijaya Widiyanto Politeknik Indonusa Surakarta
Keywords: X-Ray Image, Lungs Similarity, RGB Similarity, Lung comparison, Pixel Comparison

Abstract

The high death rate caused by pneumonia and Covid-19 is still quite high. Based on data released by WHO, 14% of deaths in children under 5 years old are caused by pneumonia. One of the processes carried out to help the diagnosis process is to look at lung images using X-Ray images. To obtain information about normal lung X-Ray images, Pneumonia and Covid-19, calculations are carried out using the color difference in each pixel of the X-ray image. The calculation process will provide output in the form of numbers in units of 0 to 100. This is done to facilitate the process of identifying the similarity of each X-Ray image being compared. The research stages are carried out with stages starting from adjusting the image size, then by breaking down the pixel values of the two images being compared and the process of calculating the difference in value from each pixel with the same coordinates. After calculating a combination of 30,000 combinations using 300 x-ray images, the results obtained in the form of the level of similarity between normal x-ray images and pneumonia x-ray images are the highest with a similarity percentage of 80.06%. The combination of normal images and pneumonia images is 10,000 combinations using 100 normal x-ray images and 100 pneumonia x-ray images. Normal x-ray images and covid x-ray images have a similarity of 79.18%. The combination of normal images and covid images is 10,000 combinations. The combination uses 100 normal x-ray images and 100 covid x-ray images. Pneumonia x-ray images and covid x-ray images have the lowest similarity level of 78.87%. The combination of pneumonia x-ray images and covid x-ray images is 10,000 combinations. The data used in the combination are 100 pneumonia images and 100 covid images. From the test results, the information obtained was that Accuracy was worth 0.54, Precision was worth 0.54, Recall was worth 0.59 and F1-score was worth 0.56.

Downloads

Download data is not yet available.

References

“Dashboard Covid-19.” Accessed: Oct. 05, 2024. [Online]. Available: https://dashboardcovid19.kemkes.go.id/

“Direktorat Jenderal Pelayanan Kesehatan.” Accessed: Oct. 05, 2024. [Online]. Available: https://yankes.kemkes.go.id/view_artikel/1997/world-pneumonia-day-2022

“Laporan Riskesdas 2018 Nasional”.

N. P. Ekananda and D. Riminarsih, “Identifikasi Penyakit Pneumonia Berdasarkan Citra Chest X-Ray Menggunakan Convolutional Neural Network,” Jurnal Ilmiah Informatika Komputer, vol. 27, no. 1, pp. 79–94, 2022, doi: 10.35760/ik.2022.v27i1.6487.

K. Ahammed et al., “Early Detection of Coronavirus Cases Using Chest X-ray Images Employing Machine Learning and Deep Learning Approaches.”

M. Heidari, S. Mirniaharikandehei, A. Z. Khuzani, G. Danala, Y. Qiu, and B. Zheng, “Improving the performance of CNN to predict the likelihood of COVID-19 using chest X-ray images with preprocessing algorithms,” Int J Med Inform, vol. 144, Dec. 2020, doi: 10.1016/j.ijmedinf.2020.104284.

T. Rahman et al., “Exploring the effect of image enhancement techniques on COVID-19 detection using chest X-ray images,” Comput Biol Med, vol. 132, May 2021, doi: 10.1016/j.compbiomed.2021.104319.

A. U. Haq, J. P. Li, S. Ahmad, S. Khan, M. A. Alshara, and R. M. Alotaibi, “Diagnostic approach for accurate diagnosis of covid-19 employing deep learning and transfer learning techniques through chest x-ray images clinical data in e-healthcare,” Sensors, vol. 21, no. 24, Dec. 2021, doi: 10.3390/s21248219.

K. M. Hosny, M. M. Darwish, K. Li, and A. Salah, “COVID-19 diagnosis from CT scans and chest X-ray images using low-cost Raspberry Pi,” PLoS One, vol. 16, no. 5 May, May 2021, doi: 10.1371/journal.pone.0250688.

Nur-a-alam, M. Ahsan, M. A. Based, J. Haider, and M. Kowalski, “COVID-19 detection from chest X-ray images using feature fusion and deep learning,” Sensors, vol. 21, no. 4, pp. 1–30, Feb. 2021, doi: 10.3390/s21041480.

E. M. Senan, A. Alzahrani, M. Y. Alzahrani, N. Alsharif, and T. H. H. Aldhyani, “Automated Diagnosis of Chest X-Ray for Early Detection of COVID-19 Disease,” Comput Math Methods Med, vol. 2021, 2021, doi: 10.1155/2021/6919483.

F. Kurnia and N. Barus, “Gambaran Diagnosis Dan Penatalaksanaan Pasien Pneumonia Yang Rawat Inap Bpjs Di Rsu Royal Prima Medan Tahun 2018,” 2020.

M. Yusuf, N. Auliah, and H. E. Sarambu, “Evaluasi Penggunaan Antibiotik Dengan Metode Gyssens Pada Pasien Pneumonia Di Rumah Sakit Bhayangkara Kupang Periode Juli-Desember 2019.”

A. S. Ramelina and R. Sari, “Pneumonia Pada Perempuan Usia 56 Tahun: Laporan Kasus Pneumonia in a 56-Year-Old Woman: A Case Report.”

L. M. T, J. Tarigan, and R. Pangaribuan, “Asuhan Keperawatan Gawat Darurat pada Pasien Pneumonia dengan Bersihan Jalan Nafas Tidak Efektif di Rumah Sakit Tk. II Putri Hijau Medan,” PubHealth Jurnal Kesehatan Masyarakat, vol. 2, no. 3, pp. 97–104, Feb. 2024, doi: 10.56211/pubhealth.v2i3.463.

A. Khairul Rizwan et al., “Sosialisasi pencegahan penyakit pneumonia pada anak di wilayah kerja Puskesmas Satelit Pahoman,” 2024.

P. Pelayanan Kesehatan Di Masa Pandemi, M. Halim Sukur, B. Kurniadi, and R. N. Faradillahisari, “Covid-19 Dalam Perspektif Hukum Kesehatan,” 2020.

Diah Handayani, Dwi Rendra Hadi, Fathiyah Isbaniah, Erlina Burhan, and Heidy Agustin, “Penyakit Virus Corona 2019,” Jurnal Respirologi Indonesia, vol. 40, Apr. 2020.

O. Walsyukurniat, Z. Stkip, and N. Selatan, “Gerakan Mencegah Daripada Mengobati Terhadap Pandemi Covid-19,” Jurnal Education and development, vol. 8, May 2020, [Online]. Available: https://www.sehatq.com/artikel/bahaya-virus-

Arianda Aditia, “Covid-19 : Epidemiologi, Virologi, Penularan, Gejala Klinis, Diagnosa, Tatalaksana, Faktor Risiko Dan Pencegahan,” Jurnal Penelitian Perawat Profesional, vol. 3, no. 4, Nov. 2021.

Nurul Hidayah Nasution, Arinil Hidayah, Khoirunnisa Mardiah Sari, and Wirda Cahyati, “Gambaran Pengetahuan Masyarakat Tentang Pencegahan Covid-19 Di Kecamatan Padangsidimpuan Batunadua, Kota Padangsidimpuan,” Jurnal Kesehatan Ilmiah Indonesia, vol. 6, Jun. 2021.

Yelvi Levani, Aldo Dwi Prastya, and Siska Mawaddatunnadila, “Coronavirus Disease 2019 (COVID-19): Patogenesis, Manifestasi Klinis dan Pilihan Terapi,” Jurnal Kedokteran Dan Kesehatan, vol. 17, Jan. 2021.

S. Timah, “Hubungan Penyuluhan kesehatan dengan Pencegahan covid 19 di Kelurahan kleak kecamatan Malalayang Kota Manado,” Indonesian Journal of Community Dedication, vol. 3, 2021.

M. Magnusson, J. Sigurdsson, S. E. Armansson, M. O. Ulfarsson, H. Deborah, and J. R. Sveinsson, “Creating RGB Images from Hyperspectral Images Using a Color Matching Function,” in International Geoscience and Remote Sensing Symposium (IGARSS), Institute of Electrical and Electronics Engineers Inc., Sep. 2020, pp. 2045–2048. doi: 10.1109/IGARSS39084.2020.9323397.

S. N. Gowda and C. Yuan, “ColorNet: Investigating the Importance of Color Spaces for Image Classification,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, 2019, pp. 581–596. doi: 10.1007/978-3-030-20870-7_36.

A. Abusukhon and S. AlZu’bi, New Direction of Cryptography: A Review on Text- to- Image Encryption Algorithms Based on RGB Color Value. Institute of Electrical and Electronics Engineers (IEEE), 2020.

S. Y. Kahu, R. B. Raut, and K. M. Bhurchandi, “Review and evaluation of color spaces for image/video compression,” Feb. 01, 2019, John Wiley and Sons Inc. doi: 10.1002/col.22291.

S. Dutta and B. B. Chaudhuri, “A color edge detection algorithm in RGB color space,” in ARTCom 2009 - International Conference on Advances in Recent Technologies in Communication and Computing, 2009, pp. 337–340. doi: 10.1109/ARTCom.2009.72.

I. Septiyanti, M. A. Khalif, and E. D. Anwar, “Analisis Dosis Paparan Radiasi Pada General X-Ray II Di Instalasi Radiologi Rumah Sakit Muhammadiyah Semarang,” Jurnal Imejing Diagnostik (JImeD), vol. 6, 2020, [Online]. Available: http://ejournal.poltekkes-smg.ac.id/ojs/index.php/jimed/index

R. Mogaveera, R. Maur, Z. Qureshi, and Y. Mane, “Multi-class Chest X-ray classification of Pneumonia, Tuberculosis and Normal X-ray images using ConvNets,” ITM Web of Conferences, vol. 44, p. 03007, 2022, doi: 10.1051/itmconf/20224403007.

F. S. Hassan and A. Gutub, “Improving data hiding within colour images using hue component of HSV colour space,” CAAI Trans Intell Technol, vol. 7, no. 1, pp. 56–68, Mar. 2022, doi: 10.1049/cit2.12053.

D. Hegde, C. Desai, R. Tabib, U. B. Patil, U. Mudenagudi, and P. K. Bora, “Adaptive Cubic Spline Interpolation in CIELAB Color Space for Underwater Image Enhancement,” in Procedia Computer Science, Elsevier B.V., 2020, pp. 52–61. doi: 10.1016/j.procs.2020.04.006.

D. Hema and S. Kannan, “Interactive Color Image Segmentation using HSV Color Space,” Science and Technology Journal, vol. 7, p. 1, 2019, doi: 10.22232/stj.2019.07.01.05.

M. Lazar and A. Hladnik, “Improved Reconstruction Of The Reflectance Spectra From Rgb Readings Using Two Instead Of One Digital Camera.”

A. Wu, W. S. Zheng, S. Gong, and J. Lai, “RGB-IR Person Re-identification by Cross-Modality Similarity Preservation,” Int J Comput Vis, vol. 128, no. 6, pp. 1765–1785, Jun. 2020, doi: 10.1007/s11263-019-01290-1.

I. Gede and M. Karma, “E Determination and Measurement of Color Dissimilarity,” International Journal of Engineering and Emerging Technology, vol. 5, no. 1.

“COVID19, Pneumonia, Normal Chest Xray PA Dataset.” Accessed: Oct. 12, 2024. [Online]. Available: https://www.kaggle.com/datasets/amanullahasraf/covid19-pneumonia-normal-chest-xray-pa-dataset

D. Krstinić, M. Braović, L. Šerić, and D. Božić-Štulić, “Multi-label Classifier Performance Evaluation with Confusion Matrix,” Academy and Industry Research Collaboration Center (AIRCC), Jun. 2020, pp. 01–14. doi: 10.5121/csit.2020.100801.

M. Heydarian, T. E. Doyle, and R. Samavi, “MLCM: Multi-Label Confusion Matrix,” IEEE Access, vol. 10, pp. 19083–19095, 2022, doi: 10.1109/ACCESS.2022.3151048.

Published
2025-01-17
How to Cite
[1]
S. Pariyasto, S. ., V. Warongan, A. Sari, and W. Widiyanto, “Lung X-ray Image Similarity Analysis Using RGB Pixel Comparison Method”, JAIC, vol. 9, no. 1, pp. 161-167, Jan. 2025.
Section
Articles