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

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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/
Title: Cervical Cancer Classification Using Multi-Directional GLCM Shape-Texture Features in LBC
Description:
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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