Abstract: Cervical cancer is a primary cause of death in women throughout the world, and early identification using cell classification is essential for increasing survival rates. Timely and accurate detection of cancer cells is critical for promoting personalized treatment and clinical diagnostics. This paper introduces Conv-Jacobian Kolmogorov Arnold Network (ConvJKAN), a novel deep-learning architecture specifically designed for high-accuracy cervical cancer cell classification from cytological images. The Conv-JKAN uniquely integrates convolutional neural networks with the Jacobian Kolmogorov Arnold network, enabling enhanced feature extraction while maintaining computational efficiency. The model employs just three convolutional layers and three JKAN layers to capture intricate spatial and contextual patterns across varying resolutions, ensuring superior recognition of subtle cytopathological changes. The novelty of Conv-JKAN lies in its hierarchical decomposition approach, where JKAN dissects high-dimensional functions into low-dimensional components using Jacobian polynomial-based sensitivity analysis. This novel technique enhances both interpretability and gradient flow, addressing common challenges in deep-learning-based medical diagnostics. To validate its robustness, Conv-JKAN was rigorously tested on three public cytology datasets, achieving high classification performance: 100%, 99.75%, and 96.52% accuracy in binary classification, and 99.7%, 98.73%, and 96.35% in multi-class classification on the Mendeley LBC, SIPaKMeD, and Herlev datasets, respectively. Furthermore, we integrated explainable artificial intelligence (XAI) techniques within Conv-JKAN to provide transparent and interpretable insights into the model’s decision-making, which is crucial for real-world clinical adoption.

Key words: Cervical cancer')">Cervical cancer, Kolmogorov Arnold network, Jacobian polynomial, Explainable artificial intelligence, Cervical cytology