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2000
Volume 21, Issue 1
  • ISSN: 1573-4056
  • E-ISSN: 1875-6603
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Abstract

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.

© 2025 The Author(s). Published by Bentham Science Publishers.
This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: https://creativecommons.org/licenses/by/4.0/legalcode. This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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2025-01-01
2025-10-31

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Exploration of Cervical Cancer Image Processing and Detection Based on U-RCNNs
Curr. Med. Imaging 21, e15734056333197 (2025); https://doi.org/10.2174/0115734056333197241211162651
/content/journals/cmir/10.2174/0115734056333197241211162651
/content/journals/cmir/10.2174/0115734056333197241211162651
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  • Article Type:     Research Article
Keyword(s): Artificial intelligence; Cervical cancer; Deep learning; Early detection; Image processing; Medical imaging; Object detection; Segmentation; U-RCNNS

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