Improved Cervical Cell Detection Model Based on Hybrid-Domain Feature Pyramid Network

The automated detection of cervical cells plays a vital role in cervical cancer screening. Cancer cells typically exhibit more prominent edge features compared to normal cells. However, during the feature fusion process, blurred edges often lack precise high-frequency information, which hinders the model’s ability to accurately identify cancer cells. To address this challenge, we propose the Hybrid-Domain Feature Pyramid Network (HD-FPN), which enhances the model’s sensitivity to cellular edge information. Specifically, we propose the Frequency-Aware Sampling that preserves critical texture features by leveraging high-frequency decomposition using wavelet transform. Furthermore, we design a multi-domain feature fusion approach to progressively refine edge features and minimize background noise. Our method achieves state-of-the-art performance on the CDetector and CRIC datasets in terms of both detection accuracy and efficiency.