Sinkron : Jurnal dan Penelitian Teknik Informatika
Vol. 9 No. 4 (2025): Articles Research October 2025

Cervical Cancer Classification Using Multi-Directional GLCM Shape-Texture Features in LBC

Surmayanti, Surmayanti (Unknown)
Nozomi, Irohito (Unknown)
Aldi, Febri (Unknown)



Article Info

Publish Date
10 Oct 2025

Abstract

Alsalatie, M., Alquran, H., Mustafa, W. A., Zyout, A., Alqudah, A. M., Kaifi, R., & Qudsieh, S. (2023). A New Weighted Deep Learning Feature Using Particle Swarm and Ant Lion Optimization for Cervical Cancer Diagnosis on Pap Smear Images. Diagnostics, 13(17), 2762. https://doi.org/10.3390/diagnostics13172762 Arbyn, M., Weiderpass, E., Bruni, L., Sanjosé, S. de, Saraiya, M., Ferlay, J., & Bray, F. (2020). Estimates of incidence and mortality of cervical cancer in 2018: A worldwide analysis. The Lancet Global Health, 8(2), e191–e203. https://doi.org/10.1016/S2214-109X(19)30482-6 Attallah, O. (2023). Cervical Cancer Diagnosis Based on Multi-Domain Features Using Deep Learning Enhanced by Handcrafted Descriptors. Applied Sciences, 13(3), 1916. https://doi.org/10.3390/app13031916 Chaddad, A., & Tanougast, C. (2017). Texture Analysis of Abnormal Cell Images for Predicting the Continuum of Colorectal Cancer. Analytical Cellular Pathology, 2017(1), 8428102. https://doi.org/10.1155/2017/8428102 Díaz del Arco, C., & Saiz Robles, A. (2024). Advancements in Cytological Techniques in Cancer. In Handbook of Cancer and Immunology (pp. 1–46). Springer, Cham. https://doi.org/10.1007/978-3-030-80962-1_385-1 Garg, M., & Dhiman, G. (2021). A novel content-based image retrieval approach for classification using GLCM features and texture fused LBP variants. Neural Computing and Applications, 33(4), 1311–1328. https://doi.org/10.1007/s00521-020-05017-z Huang, X., Liu, X., & Zhang, L. (2014). A Multichannel Gray Level Co-Occurrence Matrix for Multi/Hyperspectral Image Texture Representation. Remote Sensing, 6(9), 8424–8445. https://doi.org/10.3390/rs6098424 Ikeda, K., Oboshi, W., Hashimoto, Y., Komene, T., Yamaguchi, Y., Sato, S., Maruyama, S., Furukawa, N., Sakabe, N., & Nagata, K. (2021). Characterizing the Effect of Processing Technique and Solution Type on Cytomorphology Using Liquid-Based Cytology. https://dx.doi.org/10.1159/000519335   Kaur, H., Sharma, R., & Kaur, J. (2025). Comparison of deep transfer learning models for classification of cervical cancer from pap smear images. Scientific Reports, 15(1), 3945. https://doi.org/10.1038/s41598-024-74531-0 Merlina, N., Noersasongko, E., Nurtantio, P., Soeleman, M. A., Riana, D., & Hadianti, S. (2021). Detecting the Width of Pap Smear Cytoplasm Image Based on GLCM Feature. In Y.-D. Zhang, T. Senjyu, C. SO–IN, & A. Joshi (Eds.), Smart Trends in Computing and Communications: Proceedings of SmartCom 2020 (pp. 231–239). Springer. https://doi.org/10.1007/978-981-15-5224-3_22 Mishra, G. A., Pimple, S. A., & Shastri, S. S. (2021). An overview of prevention and early detection of cervical cancers. Indian Journal of Medical and Paediatric Oncology, 32, 125–132. Plissiti, M. E., Nikou, C., & Charchanti, A. (2011). Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images. Pattern Recognition Letters, 32(6), 838–853. https://doi.org/10.1016/j.patrec.2011.01.008 Raga Permana, Z. Z., & Setiawan, A. W. (2024). Classification of Cervical Intraepithelial Neoplasia Based on Combination of GLCM and L*a*b* on Colposcopy Image Using Machine Learning. 2024 International Conference on Artificial Intelligence in Information and Communication (ICAIIC), 035–040. https://doi.org/10.1109/ICAIIC60209.2024.10463256 Rastogi, P., Khanna, K., & Singh, V. (2023, August 8). Classification of single‐cell cervical pap smear images using EfficientNet—Rastogi—2023—Expert Systems—Wiley Online Library. https://onlinelibrary.wiley.com/doi/abs/10.1111/exsy.13418 Singh, D., Vignat, J., Lorenzoni, V., Eslahi, M., Ginsburg, O., Lauby-Secretan, B., Arbyn, M., Basu, P., Bray, F., & Vaccarella, S. (2023). Global estimates of incidence and mortality of cervical cancer in 2020: A baseline analysis of the WHO Global Cervical Cancer Elimination Initiative. The Lancet Global Health, 11(2), e197–e206. https://doi.org/10.1016/S2214-109X(22)00501-0 Singh, T. G., & Karthik, B. (2023). Accurate Cervical Tumor Cell Segmentation and Classification from Overlapping Clumps in Pap Smear Images. In S. N. Singh, S. Mahanta, & Y. J. Singh (Eds.), Proceedings of the NIELIT’s International Conference on Communication, Electronics and Digital Technology (pp. 659–673). Springer Nature. https://doi.org/10.1007/978-981-99-1699-3_46 Strander, B., Andersson-Ellström, A., Milsom, I., Rådberg, T., & Ryd, W. (2007). Liquid-based cytology versus conventional Papanicolaou smear in an organized screening program. Cancer Cytopathology, 111(5), 285–291. https://doi.org/10.1002/cncr.22953 Wahidin, M., Febrianti, R., Susanty, F., & Hasanah, S. R. (2022, March 1). Twelve Years Implementation of Cervical and Breast Cancer Screening Program in Indonesia—PMC. https://pmc.ncbi.nlm.nih.gov/articles/PMC9360967/

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Journal Info

Abbrev

sinkron

Publisher

Subject

Computer Science & IT

Description

Scope of SinkrOns Scientific Discussion 1. Machine Learning 2. Cryptography 3. Steganography 4. Digital Image Processing 5. Networking 6. Security 7. Algorithm and Programming 8. Computer Vision 9. Troubleshooting 10. Internet and E-Commerce 11. Artificial Intelligence 12. Data Mining 13. Artificial ...