Classification of Cervical Cell Types Based on Machine Learning Approach: A Comparative Study

  • Wan Azani Wan Mustafa Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia; Advanced Computing (AdvComp), Centre of Excellence (CoE), Universiti Malaysia Perlis (UniMAP), Campus Pauh Putra, 02600 Arau, Perlis, Malaysia https://orcid.org/0000-0001-7260-6085
  • Khalis Khiruddin Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia https://orcid.org/0009-0002-7317-8435
  • Khairur Rijal Jamaludin Faculty of Artificial Intelligence, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, 54100 Kuala Lumpur, Malaysia
  • Firdaus Yuslan Khusairi Faculty of Electrical Engineering & Technology, Universiti Malaysia Perlis, Pauh Putra Campus, 02600 Arau, Perlis, Malaysia
  • Shahrina Ismail Faculty of Science and Technology, Universiti Sains Islam Malaysia (USIM), Negeri Sembilan, Bandar Baru Nilai, 71800, Malaysia
Keywords: Cervical cell classification; Convolutional Neural Network; Image segmentation; Support Vector Machine; K Nearest Neighbours.

Abstract

Cervical cancer remains a major global health issue and is the second most common cancer affecting women worldwide. Early detection is crucial for effective treatment but remains challenging due to the asymptomatic nature of the disease and the visual complexity of cervical cell structures, which are often affected by inconsistent staining, poor contrast, and overlapping cells. This study aims to classify cervical cell images using Artificial Intelligence (AI) techniques by comparing the performance of Convolutional Neural Networks (CNN), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN). The Herlev Pap smear image dataset was used for experimentation. In the preprocessing phase, images were resized to 100 × 100 pixels and enhanced through grayscale conversion, Gaussian smoothing for noise reduction, contrast stretching, and intensity normalization. Segmentation was performed using region-growing and active contour methods to accurately isolate cell nuclei. All classifiers were implemented using MATLAB. Experimental results show that CNN achieved the highest performance, with an accuracy of 85%, precision of 86.7%, and sensitivity of 83%, outperforming both SVM and KNN. These findings indicate that CNN is the most effective approach for cervical cell classification in this study. However, limitations such as class imbalance and occasional segmentation inconsistencies impacted overall performance, particularly in detecting abnormal cells. Future work will focus on improving classification accuracy especially for abnormal samples by exploring data augmentation techniques such as Generative Adversarial Networks (GANs) and implementing ensemble learning strategies. Additionally, integrating the proposed system into a real-time diagnostic platform using a graphical user interface (GUI) could support clinical decision-making and enhance cervical cancer screening programs.

Downloads

Download data is not yet available.

References

World Health Organization, “Cervical cancer,” Geneva, Mar. 05, 2024.

A. Petca, A. Borislavschi, M. Zvanca, R.-C. Petca, F. Sandru, and M. Dumitrascu, “Non-sexual HPV transmission and role of vaccination for a better future (Review),” Exp Ther Med, vol. 20, no. 6, 2020, doi: 10.3892/etm.2020.9316.

K. S. Okunade, “Human papillomavirus and cervical cancer,” J Obstet Gynaecol (Lahore), vol. 40, no. 5, pp. 602–608, 2020, doi: 10.1080/01443615.2019.1634030.

W. A. Mustafa, S. Ismail, F. S. Mokhtar, H. Alquran, and Y. Al-Issa, “Cervical Cancer Detection Techniques: A Chronological Review,” 2023. doi: 10.3390/diagnostics13101763.

H. Bandyopadhyay and M. Nasipuri, “Segmentation of Pap Smear Images for Cervical Cancer Detection,” 2020 IEEE Calcutta Conference, CALCON 2020 - Proceedings, pp. 30–33, Feb. 2020, doi: 10.1109/CALCON49167.2020.9106484.

M. I. Waly, M. Y. Sikkandar, M. A. Aboamer, S. Kadry, and O. Thinnukool, “Optimal deep convolution neural network for cervical cancer diagnosis model,” Computers, Materials and Continua, vol. 70, no. 2, 2022, doi: 10.32604/cmc.2022.020713.

W. A. Mustafa, A. Halim, M. W. Nasrudin, and K. S. A. Rahman, “Cervical cancer situation in Malaysia: A systematic literature review,” 2022. doi: 10.32604/biocell.2022.016814.

T. Vaiyapuri et al., “Modified metaheuristics with stacked sparse denoising autoencoder model for cervical cancer classification,” Computers and Electrical Engineering, vol. 103, 2022, doi: 10.1016/j.compeleceng.2022.108292.

K. Prasad Battula and B. S. Chandana, “Deep Learning based Cervical Cancer Classification and Segmentation from Pap Smears Images using an EfficientNet,” 2022. [Online]. Available: www.ijacsa.thesai.org

A. Rasheed, S. H. Shirazi, A. I. Umar, M. Shahzad, W. Yousaf, and Z. Khan, “Cervical cell’s nucleus segmentation through an improved UNet architecture,” PLoS One, vol. 18, no. 10 October, 2023, doi: 10.1371/journal.pone.0283568.

E. Zhang et al., “Cervical cell nuclei segmentation based on GC-UNet,” Heliyon, vol. 9, no. 7, p. e17647, Jul. 2023, doi: 10.1016/J.HELIYON.2023.E17647.

M. M. Rahaman et al., “DeepCervix: A deep learning-based framework for the classification of cervical cells using hybrid deep feature fusion techniques,” Comput Biol Med, vol. 136, p. 104649, Sep. 2021, doi: 10.1016/J.COMPBIOMED.2021.104649.

B. Z. Wubineh, A. Rusiecki, and K. Halawa, “Classification of cervical cells from the Pap smear image using the RES_DCGAN data augmentation and ResNet50V2 with self-attention architecture,” Neural Comput Appl, vol. 36, no. 34, pp. 21801–21815, Dec. 2024, doi: 10.1007/S00521-024-10404-X/FIGURES/8.

C. W. Zhang, D. Y. Jia, N. K. Wu, Z. G. Guo, and H. R. Ge, “Quantitative detection of cervical cancer based on time series information from smear images,” Appl Soft Comput, vol. 112, p. 107791, Nov. 2021, doi: 10.1016/J.ASOC.2021.107791.

L. Nanni, S. Ghidoni, S. Brahnam, S. Liu, and L. Zhang, “Ensemble of handcrafted and deep learned features for cervical cell classification,” Intelligent Systems Reference Library, vol. 186, pp. 117–135, 2020, doi: 10.1007/978-3-030-42750-4_4.

N. Wu, D. Jia, C. Zhang, and Z. Li, “Cervical cell classification based on strong feature CNN-LSVM network using Adaboost optimization,” Journal of Intelligent and Fuzzy Systems, vol. 44, no. 3, pp. 4335–4355, 2023, doi: 10.3233/JIFS-221604.

H. Alquran, W. Azani Mustafa, F. F. Mohammed, and A. Alkhayyat, “Nucleus Detection Using Deep Learning Approach on Pap Smear Images,” in 2023 6th International Conference on Engineering Technology and its Applications (IICETA), IEEE, Jul. 2023, pp. 665–668. doi: 10.1109/IICETA57613.2023.10351221.

W. A. Mustafa, L. Z. Wei, and K. S. Ab Rahman, “Automated cell nuclei segmentation on cervical smear images using structure analysis,” Journal of Biomimetics, Biomaterials and Biomedical Engineering, vol. 51, 2021, doi: 10.4028/www.scientific.net/JBBBE.51.105.

A. Ghoneim, G. Muhammad, and M. S. Hossain, “Cervical cancer classification using convolutional neural networks and extreme learning machines,” Future Generation Computer Systems, vol. 102, pp. 643–649, Jan. 2020, doi: 10.1016/J.FUTURE.2019.09.015.

T. Conceição, C. Braga, L. Rosado, and M. J. M. Vasconcelos, “A Review of Computational Methods for Cervical Cells Segmentation and Abnormality Classification,” International Journal of Molecular Sciences 2019, Vol. 20, Page 5114, vol. 20, no. 20, p. 5114, Oct. 2019, doi: 10.3390/IJMS20205114.

A. Mohammed and R. Kora, “A comprehensive review on ensemble deep learning: Opportunities and challenges,” Journal of King Saud University - Computer and Information Sciences, vol. 35, no. 2, pp. 757–774, Feb. 2023, doi: 10.1016/J.JKSUCI.2023.01.014.

I. Mansoury, D. El Bourakadi, A. Yahyaouy, and J. Boumhidi, “Optimized extreme learning machine using genetic algorithm for short-term wind power prediction,” Bulletin of Electrical Engineering and Informatics, vol. 13, no. 2, pp. 1334–1343, Apr. 2024, doi: 10.11591/EEI.V13I2.6476.

H. Wei et al., “A texture feature extraction method considering spatial continuity and gray diversity,” International Journal of Applied Earth Observation and Geoinformation, vol. 130, p. 103896, Jun. 2024, doi: 10.1016/J.JAG.2024.103896.

I. Chaabane, R. Guermazi, and M. Hammami, “Enhancing techniques for learning decision trees from imbalanced data,” Adv Data Anal Classif, vol. 14, no. 3, pp. 677–745, Sep. 2020, doi: 10.1007/S11634-019-00354-X/TABLES/19.

E. Gokgoz and A. Subasi, “Comparison of decision tree algorithms for EMG signal classification using DWT,” Biomed Signal Process Control, vol. 18, 2015, doi: 10.1016/j.bspc.2014.12.005.

M. E. Plissiti, P. Dimitrakopoulos, G. Sfikas, C. Nikou, O. Krikoni, and A. Charchanti, “Sipakmed: A New Dataset for Feature and Image Based Classification of Normal and Pathological Cervical Cells in Pap Smear Images,” Proceedings - International Conference on Image Processing, ICIP, pp. 3144–3148, Aug. 2018, doi: 10.1109/ICIP.2018.8451588.

H. Alquran et al., “Cervical Cancer Classification Using Combined Machine Learning and Deep Learning Approach,” Computers, Materials and Continua, vol. 72, no. 3, pp. 5117–5134, 2022, doi: 10.32604/CMC.2022.025692.

V. K. Chandu, B. K. Sairam, and S. J. Prakash, “Cervical Cancer Cell Detection Using Image Processing and Matlab,” 2024.

P. Wang, L. Wang, Y. Li, Q. Song, S. Lv, and X. Hu, “Automatic cell nuclei segmentation and classification of cervical Pap smear images,” Biomed Signal Process Control, vol. 48, pp. 93–103, Feb. 2019, doi: 10.1016/J.BSPC.2018.09.008.

N. B. Byju, • Vilayil, K. Sujathan, P. Malm, and • R Rajesh Kumar, “A fast and reliable approach to cell nuclei segmentation in PAP stained cervical smears,” CSI Transactions on ICT 2013 1:4, vol. 1, no. 4, pp. 309–315, Oct. 2013, doi: 10.1007/S40012-013-0028-Y.

L. Sukel and L. Sukesh, “Detection of Cervical Cancer Using Gaussian Filter and Canny Edge Detection Algorithm,” International Research Journal of Engineering and Technology, vol. 7209, 2008, doi: 10.1128/CMR.16.1.1-17.

C. Bergmeir, M. García Silvente, and J. M. Benítez, “Segmentation of cervical cell nuclei in high-resolution microscopic images: A new algorithm and a web-based software framework,” Comput Methods Programs Biomed, vol. 107, no. 3, pp. 497–512, Sep. 2012, doi: 10.1016/J.CMPB.2011.09.017.

P. Bamford and B. Lovell, “Unsupervised cell nucleus segmentation with active contours,” Signal Processing, vol. 71, no. 2, pp. 203–213, Dec. 1998, doi: 10.1016/S0165-1684(98)00145-5.

P. Cunningham and S. J. Delany, “k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples),” ACM Comput Surv, vol. 54, no. 6, Apr. 2020, doi: 10.1145/3459665.

P. K. Syriopoulos, N. G. Kalampalikis, S. B. Kotsiantis, and M. N. Vrahatis, “kNN Classification: a review,” Ann Math Artif Intell, pp. 1–33, Sep. 2023, doi: 10.1007/S10472-023-09882-X/METRICS.

S. Suthaharan, “Machine Learning Models and Algorithms for Big Data Classification,” vol. 36, 2016, doi: 10.1007/978-1-4899-7641-3.

M. Khorshid, T. H. M. Abou-El-Enien, and G. M. A. Soliman, “A Comparison among Support Vector Machine and other Machine Learning Classification Algorithms,” 2015.

S. Abe, “Support Vector Machines for Pattern Classification,” 2010, doi: 10.1007/978-1-84996-098-4.

A. K. Jain, R. P. W. Duin, and J. Mao, “Statistical pattern recognition: A review,” IEEE Trans Pattern Anal Mach Intell, vol. 22, no. 1, pp. 4–37, Jan. 2000, doi: 10.1109/34.824819.

M. Awad and R. Khanna, “Support Vector Machines for Classification,” Efficient Learning Machines, pp. 39–66, 2015, doi: 10.1007/978-1-4302-5990-9_3.

K. O’Shea and R. Nash, “An Introduction to Convolutional Neural Networks,” Int J Res Appl Sci Eng Technol, vol. 10, no. 12, pp. 943–947, Nov. 2015, doi: 10.22214/ijraset.2022.47789.

X. Zhao, L. Wang, Y. Zhang, X. Han, M. Deveci, and M. Parmar, “A review of convolutional neural networks in computer vision,” Artif Intell Rev, vol. 57, no. 4, pp. 1–43, Apr. 2024, doi: 10.1007/S10462-024-10721-6/FIGURES/33.

A. Khan, A. Sohail, U. Zahoora, and A. S. Qureshi, “A survey of the recent architectures of deep convolutional neural networks,” Artificial Intelligence Review 2020 53:8, vol. 53, no. 8, pp. 5455–5516, Apr. 2020, doi: 10.1007/S10462-020-09825-6.

Z. Zheng, Z. Chen, F. Hu, J. Zhu, Q. Tang, and Y. Liang, “An automatic diagnosis of arrhythmias using a combination of CNN and LSTM technology,” Electronics (Switzerland), vol. 9, no. 1, 2020, doi: 10.3390/electronics9010121.

A. Desiani, M. Erwin, B. Suprihatin, S. Yahdin, A. I. Putri, and F. R. Husein, “Bi-path Architecture of CNN Segmentation and Classification Method for Cervical Cancer Disorders Based on Pap-smear Images,” IAENG Int J Comput Sci, vol. 48, no. 3, 2021.

Published
2025-05-20
How to Cite
[1]
W. A. Wan Mustafa, K. Khiruddin, K. R. Jamaludin, F. Y. Khusairi, and S. Ismail, “Classification of Cervical Cell Types Based on Machine Learning Approach: A Comparative Study”, j.electron.electromedical.eng.med.inform, vol. 7, no. 3, pp. 646-662, May 2025.
Section
Medical Engineering