Comparative Analysis of Machine Learning Methods on Cervical Cytology Images
Year: 2026
Type: preprint
Abstract: Cervical cancer is the fourth most common cancer in women worldwide, although it is largely preventable through early diagnosis. Expert reading of Pap smear cytology is time-consuming and subject to inter-observer variability. This study compares three approaches on 7,341 Pap smear images from four merged public datasets labelled with five Bethesda classes (NILM, ASCUS, LSIL, HSIL, SCC): classical machine learning with handcrafted colour, texture and shape features; classical machine learning with deep features extracted from ImageNet-pretrained convolutional neural networks (EfficientNet-B0, ResNet50); and an end-to-end fine-tuned EfficientNet-B0. An MD5 hash check showed that 933 images belonged to duplicate groups and that 112 hashes appeared with conflicting labels; 6,620 images remained after cleaning. Twenty-six configurations built with eight classifiers were evaluated on a shared stratified test set of 993 images. The fine-tuned EfficientNet-B0 performed best, with 84.99% accuracy, a macro F1 of 0.843, a ROC-AUC of 0.968 and a quadratic weighted kappa of 0.914. Among the classical pipelines, the SVM on EfficientNet-B0 features gave the best result (macro F1 0.746). In a normal-versus-abnormal screening setting, the best model reached 95.2% sensitivity and 85.1% specificity. ASCUS was the hardest class to separate. Evidence that source-specific image properties may lead to shortcut learning is also discussed. Preprint, not peer reviewed. Code: https://www.kaggle.com/code/tolga0z/cervical-cancer-ml-model-comparison
Author: Özlen, Tolga
Institution: Istanbul Aydın University
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7225661215
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