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Exploration of Cervical Cancer Image Processing and Detection Based on URCNNs
Cheng Cheng1, Yi Yang2, Youshan Qu3
1Changchun University of Science and Technology,7089 Weixing Road, Changchun City, Jilin Province, China.
Background:
Cervical cancer is a prevalent malignancy among women, often asymptomatic in early stages, complicating detection.
Objective:
This study aims to investigate innovative techniques for early cervical cancer detection using a novel U-RCNNS model.
Methods:
Cervical 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.
Results:
The 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.
Conclusion:
The 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.
