ArticleCurrent medical imaging2025
Exploration of Cervical Cancer Image Processing and Detection Based on URCNNs.
Article in Current medical imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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.
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