Leveraging Deep Learning for Early Detection of Cervical Cancer and Dysplasia in China Using U-NET++ and RepVGG Networks (Preprint)
Authors: Li Baoqing , Chen Lulu , Sun Chudi , Wang Jian , Ma Sicong , Xu Hang , Wang Luyao , Rong Taotao , Hu Qun , Wei Jie , Lu Lijuan , Bai Guannan , Liu Zhangdaihong , Luo Peng , Ye Guoliu , Zhang Lin 
Published: January 2025

BACKGROUND

Cervical cancer remains a significant global public health concern, driven primarily by persistent infections with high-risk human papillomavirus (HPV). The burden is significantly heavier in low- and middle-income regions, such as rural areas of China, where restricted access to screening and vaccinations heighten incidence and mortality rates. Cervical cancer is a malignant tumor that can be prevented, screened, diagnosed and treated early, and even eliminated. This study leverages deep learning to enhance the early detection of cervical cancer by improving the diagnostic accuracy of colposcopic image analysis.

OBJECTIVE

The aim of this study is to leverage deep learning techniques to improve the early detection of cervical cancer through the enhancement of colposcopic image diagnostic accuracy.

METHODS

The study sourced a comprehensive dataset of colposcopic images from The First Affiliated Hospital of Bengbu Medical University, with each image manually annotated by expert clinicians. The U-NET++ architecture was employed for precise image segmentation, converting colposcopic images into binary representations for detailed analysis. The RepVGG framework was then applied for classification, focusing on detecting cervical cancer, HPV infections, and cervical intraepithelial neoplasia (CIN). From a dataset of 848 subjects, 424 high-quality images were selected for training, with the remaining 424 used for validation. Pre-processing techniques were applied to normalize the data, enhancing model generalizability. The model was evaluated based on sensitivity, specificity, and predictive accuracy.

RESULTS

The deep learning model effectively identified the disease severity in colposcopic images, achieving a predictive accuracy of 83.01%. Among the 424 validation subjects, cervical pathology was correctly identified in 352, demonstrating high diagnostic precision. The model excelled in detecting early-stage lesions, including CIN I and CIN II, which are crucial for initiating timely interventions. This capability positions the model as a valuable tool for reducing cervical cancer incidence and improving patient outcomes, especially in areas lacking traditional diagnostic infrastructure.

CONCLUSIONS

The integration of deep learning into colposcopic image analysis marks a significant advancement in early cervical cancer detection. The study suggests that AI-driven diagnostic tools can significantly improve screening accuracy, particularly in resource-limited settings with scarce specialized expertise. Reducing reliance on human interpretation minimizes variability and enhances efficiency. In rural and underserved areas, the deployment of AI-based solutions could be transformative, potentially reducing cervical cancer incidence and mortality. With further refinement, these models could be adapted for broader population screening, aiding global efforts to eliminate cervical cancer as a public health threat.