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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.

© 2024 The Author(s). Published by Bentham Science.
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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/content/journals/cmir/10.2174/0115734056333197241211162651
2025-01-02
2025-01-18

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/content/journals/cmir/10.2174/0115734056333197241211162651
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Exploration of Cervical Cancer Image Processing and Detection Based on U-RCNNs
Bentham Science Publishers , e15734056333197; 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
Keywords: Early detection ; Object detection ; Deep learning ; Cervical cancer ; Medical imaging ; Image processing ; U-RCNNS ; Segmentation ; Artificial intelligence

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