Enhanced cervical cancer classification using convolutional tsetlin machines with transfer learning

Abstract

Abstract Cervical cancer remains a major global health challenge, particularly in low-resource settings where access to timely and accurate diagnostic services is limited. Although Pap smear screening has significantly reduced cervical cancer mortality, manual cytological assessment is labor-intensive, prone to inter-observer variability, and dependent on expert availability. To address these limitations, this study presents a hybrid classification framework that integrates deep feature extraction with interpretable rule-based learning for cervical cancer diagnosis from Pap smear images. High-level visual features are extracted using a pre-trained InceptionV3 convolutional neural network, while classification is performed using Convolutional Tsetlin Machines (CTMs), which employ logical clauses learned through Tsetlin automata to enable transparent decision-making. The proposed framework was evaluated on a publicly available liquid-based cytology dataset comprising four diagnostic classes: Negative for Intraepithelial Malignancy, Low Squamous Intraepithelial Lesion, High Squamous Intraepithelial Lesion, and Squamous Cell Carcinoma. Experimental results obtained under stratified cross-validation demonstrate strong and consistent performance, achieving an accuracy of 99.96%, precision of 98.99%, recall of 98.96%, and an F1-score of 98.98%. Beyond high classification accuracy, the intrinsic interpretability of CTMs allows inspection of learned logical rules, supporting clinical transparency and trust. These findings indicate that combining deep transfer learning with interpretable machine learning offers a promising direction for reliable and explainable cervical cancer screening systems, particularly in resource-constrained healthcare environments.