Exploration of Cervical Cancer Image Processing and Detection Based on U-RCNNs - Cheng, Cheng; Yang, Yi; Qu, Youshan (Current Medical Imaging, 2025)
BackgroundCervical cancer is a prevalent malignancy among women, often asymptomatic in early stages, complicating detection.ObjectiveThis study aims to investigate innovative techniques for early cervical cancer detection using a novel U-RCNNS model.MethodsCervical epithelial cell images stained with hematoxylin and eosin (HE) were analyzed using the U-RCNNS model, which integrates U-Net for segmentation and R-CNN for object detection, incorporating dilated convolution techniques.ResultsThe U-RCNNS model significantly improved the accuracy of detecting and segmenting cervical cancer cells, with the enhanced Mask R-CNN showing notable advancements over the baseline model.ConclusionThe U-RCNNS model presents a promising solution for early cervical cancer detection, offering improved accuracy compared to traditional methods and highlighting its potential for clinical application in early diagnosis.
Keywords: Early detection