Leveraging Deep Learning for Early Detection of Cervical Cancer and Dysplasia in China Using U-NET++ and RepVGG Networks

Baoqing Li¹Lulu Chen²Chudi Sun³Jian Wang⁴Sicong Ma⁴Hang Xu⁵Luyao Wang⁶Taotao Rong⁶Qun Hu⁶Jie Wei⁶Lijuan Lu⁷Guannan Bai⁸Zhangdaihong Liu⁹Peng Luo¹⁰Aimin Xu¹¹Li Liu¹²Guoliu Ye⁶Lin Zhang^4*

• ¹The Second Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
• ²The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
• ³Soochow University, Suzhou, China
• ⁴Monash University, Melbourne, Australia
• ⁵The University of Sydney, Sydney, Australia
• ⁶the First Affiliated Hospital of Bengbu Medical University, Bengbu, China
• ⁷Nantong University Affiliated Jiangyin Hospital, Nantong, China
• ⁸Zhejiang University School of Medicine, Zhejiang, China
• ⁹University of Oxford, Oxford, United Kingdom
• ¹⁰Southern Medical University, Guangzhou, China
• ¹¹Zhejiang Wanli University, Zhejiang, China
• ¹²Affiliated Hospital of Jiangnan University, Wuxi, China

Background: Cervical cancer is a significant global public health issue, primarily caused by persistent high-risk human papillomavirus (HPV) infections. The disease burden is disproportionately higher in low- and middle-income regions, such as rural China, where limited access to screening and vaccinations leads to increased incidence and mortality rates. Cervical cancer is preventable and treatable when detected early; this study utilizes deep learning to enhance early detection 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. 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. Conclusion: 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. 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.