References
- Alquran, H., Alsalatie, M., Mustafa, W. A., Abdi, R. A., and Ismail, A. (2022). Cervical Net: A novel cervical cancer classification using feature fusion, Bioengineering 9(10): 578, DOI: 10.3390/bioengineering9100578.
- Awais, M., Akram, T., Alasiry, A., Marzougui, M., Park, J. and Chang, B. (2024). An expert system for leukocyte classification using probabilistic deep feature optimization via distribution estimation, International Journal of Applied Mathematics and Computer Science 34(4): 579–595, DOI: 10.61822/amcs-2024-0039.
- Cristianini, N. and Shawe-Taylor, J. (2000). An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, Cambridge.
- Devi, G.M. and Neelambary, V. (2022). Computer-aided diagnosis of white blood cell leukemia using VGG16 convolution neural network, 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA), Coimbatore, India, pp. 1064–1068.
- Duda, R., Hart, P. and Stork, D. (2000). Pattern Classification, 2nd Edn, Wiley Interscience Publishers, New York.
- Fang, M., Fu, M., Liao, B., Lei, X. and Wu, F.-X. (2024). Deep integrated fusion of local and global features for cervical cell classification, Computers in Biology and Medicine 171(C): 108153.
- Freytes, C.Y., Perry Mayrand, R., Sawada, L.O., Yan Liang, T., Curiel Cid, R.E., Burke, S., Loewenstein, D., Duara, R. and Adjouadi, M. (2023). Recursive feature elimination with cross validation for Alzheimer’s disease classification using cognitive exam scores, 2023 Intelligent Methods, Systems, and Applications (IMSA) Conference, Giza, Egypt, pp. 327–332.
- Guyon, I. and Elisseeff, A. (2003). An introduction to variable and feature selection, Journal of Machine Learning Research 3(3): 1157–1182.
- Hut, I., Jeftic, B., Dragicevic, A., Matija, L. and Koruga, D. (2022). Computer-aided diagnostic system for whole slide imaging of liquid-based cervical cytology sample classification using convolutional neural networks, Contemporary Materials 13(2): 169–177.
- Jeleń, Ł., Stankiewicz-Antosz, I., Chosia, M. and Jeleń, M. (2025). Optimizing cervical cancer diagnosis with feature selection and deep learning, Applied Sciences 15(3): 1458.
- Kanavati, F., Hirose, N., Ishii, T., Fukuda, A., Ichihara, S. and Tsuneki, M. (2022). A deep learning model for cervical cancer screening on liquid-based cytology specimens in whole slide images, Cancers 14(5): 1159.
- Khoulqi, I. and Idrissi, N. (2022). Deep learning-based cervical cancer classification, 2022 International Conference on Technology Innovations for Healthcare, Magdeburg, Germany, pp. 30–33.
- Kitchener, H., Almonte, M., Thomson, C., Wheeler, P., Sargent, A., Stoykova, B., Gilham, B., Baysson, H., Roberts, C., Dowie, R., Desai, M., Mather, J., Bailey, A., Turner, A., Moss, S. and Peto, J. (2009). HPV testing in combination with liquid-based cytology in primary cervical screening (artistic): A randomised controlled trial, The Lancet Oncology 10(7): 672–682.
- Klug, S., Neis, K., Harlfinger, W., Malter, A., König, J., Spieth, S., Brinkmann-Smetanay, F., Kommoss, F., Weyer, V. and Ikenberg, H. (2013). A randomized trial comparing conventional cytology to liquid-based cytology and computer assistance, International Journal of Cancer 132(12): 2849–2857.
- Kowal, M., Filipczuk, P., Obuchowicz, A., Korbicz, J. and Monczak, R. (2013). Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images, Computers in Biology and Medicine 43(10): 1563–1572.
- Liew, X.Y., Hameed, N. and Clos, J. (2021). An investigation of XGBOOST-based algorithm for breast cancer classification, Machine Learning with Applications 6: 100154.
- Mariarputham, E.J. and Stephen, A. (2015). Nominated texture based cervical cancer classification, Computational and Mathematical Methods in Medicine 2015(1): 586928, DOI: 10.1155/2015/586928.
- Mathivanan, S.K., Francis, D., Srinivasan, S., Khatavkar, V.P.K. and Shah, M.A. (2024). Enhancing cervical cancer detection and robust classification through a fusion of deep learning models, Scientific Reports 14(1): 10812.
- Mazurek, P. and Oszutowska-Mazurek, D. (2014). From the slit-island method to the Ising model: Analysis of irregular grayscale objects, International Journal of Applied Mathematics and Computer Science 24(1): 49–63, DOI: 10.2478/amcs-2014-0004.
- Molnar, C., Casalicchio, G. and Bischl, B. (2020). Interpretable machine learning—A brief history, state-of-the-art and challenges, in I. Koprinska et al. (Eds) ECML PKDD 2020 Workshops, Communications in Computer and Information Science, Vol. 1323, Springer, Cham, pp. 417–431.
- Polish National Cancer Registry (2021). Incidence statistics for 2021, http://onkologia.org.pl/nowotwory-szyjki-macicy/.
- Nithya, B. and Ilango, V. (2019). Evaluation of machine learning based optimized feature selection approaches and classification methods for cervical cancer prediction, SN Applied Sciences 1(6): 641.
- Ojala, T., Pietikäinen, M. and Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions, Pattern Recognition 29(1): 51–59.
- Qu, H., Yan, Z., Wu, W., Chen, F., Ma, C., Chen, Y., Wang, J. and Lu, X. (2021). Rapid diagnosis and classification of cervical lesions by serum infrared spectroscopy combined with machine learning, in X. Wei and L. Liu (Eds), AOPC 2021: Biomedical Optics, International Society for Optics and Photonics, Bellingham, p. 120670A.
- Rahmadwati, Naghdy, G., Ros, M., Todd, C. and Norahmawati, E. (2011). Cervical cancer classification using Gabor filters, 2011 1st IEEE International Conference on Health-care Informatics, Imaging and Systems Biology (HISB 2011), San Jose, USA, pp. 48–52.
- Rajeev, M.A. (2021). A framework for detecting cervical cancer based on UD-MHDC segmentation and MBD-RCNN classification techniques, 2021 2nd Global Conference for Advancement in Technology, Bangalore, India, pp. 1–9.
- Ramkumar, G. and Sajiv, G. (2023). Histopathological image analysis for breast cancer identification using gradient boosting classifier, 2023 International Conference on Self Sustainable Artificial Intelligence Systems, Erode, India, pp. 789–796.
- Sabeena, K. and Gopakumar, C. (2022). A hybrid model for efficient cervical cell classification, Biomedical Signal Processing and Control 72(Part A): 103288, DOI: 10.1016/j.bspc.2021.103288.
- Sajiv, G. and Ramkumar, G. (2023). A robust breast cancer classification model using extra-trees classifier for histopathological image, 2023 International Conference on Advances in Computing, Communication and Applied Informatics, Chennai, India, pp. 1–7.
- Sharma, P., Verma, G. and Sinha, R. (2024). Deep ensemble attention framework for cervical cytology image classification, SN Computer Science 5(3): 185.
- Sornapudi, S., Brown, G., Xue, Z., Long, R., Allen, L. and Antani, S. (2020). Comparing deep learning models for multi-cell classification in liquid-based cervical cytology images, AMIA Annual Symposium Proceedings 2019: 820–827.
- Stankiewicz, I. (2018). Using a Computer Program to Evaluate Cytological Smears Received from the Vaginal Part of the Cervix Using the LBC Method, Master’s thesis, Wroclaw Medical University, Wroclaw.
- Su, Z., Pietikäinen, M. and Liu, L. (2023). From local binary patterns to pixel difference networks for efficient visual representation learning, in R. Gade and M. Felsberg (Eds), Image Analysis, Springer Nature Switzerland, Cham, pp. 138–155.
- Subarna, T. and Sukumar, P. (2022). Detection and classification of cervical cancer images using CEENET deep learning approach, Journal of Intelligent & Fuzzy Systems 43(3): 3695–3707.
- Tarawneh, A.S., Celik, C., Hassanat, A.B. and Chetverikov, D. (2020). Detailed investigation of deep features with sparse representation and dimensionality reduction in CBIR: A comparative study, Intelligent Data Analysis: An International Journal 24(1): 47–68, DOI: 10.3233/IDA-184411.
- Uthayakumar, J., Easwaramoorthy, V. and Kannan, R. (2018). Pap smear image classification using local binary patterns and support vector machine, Journal of Medical Systems 42(9): 163.
- Wild, C.P., Weiderpass, E. and Stewart, B.W. (Eds) (2020). World Cancer Report: Cancer Research for Cancer Prevention, IARC, Lyon.
- Wong, L., Ccopa, A., Diaz, E., Valcarcel, S., Mauricio, D. and Villoslada, V. (2023). Deep learning and transfer learning methods to effectively diagnose cervical cancer from liquid-based cytology Pap smear images, International Journal of Online and Biomedical Engineering 19(04): 77–93.