Wan Azani MustafaKhalis KhiruddinKhairur Rijal JamaludinFirdaus Yuslan KhusairiShahrina Ismail2026-02-232026-02-232025Wan Azani Mustafa, Khalis Khiruddin, Khairur Rijal Jamaludin, Firdaus Yuslan Khusairi and Shahrina Ismail (2025) Comparative Analysis of Cervical Cell Classification Using Machine Learning Algorithms. Journal of Electronics, Electromedical Engineering, and Medical Informatics, 7(3). https://doi.org/10.35882/jeeemi.v7i2.6522656-86322243-2910.35882/jeeemi.v7i2.652https://istaff.usim.edu.my/portaldoc/akademik/jurnal/2243/byine.pdfhttps://oarep.usim.edu.my/handle/123456789/29218Indexed by SCOPUSCervical 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.en-USCervical cell classificationConvolutional Neural NetworkImage segmentationSupport Vector MachineK Nearest Neighbourstext::journal::journal article64666273