A novel ensemble framework based on CNN models and Swin transformer for cervical cytology image classification

Yangfan Liu; Zifeng Wang; Anjun Dai; Wenhao Gu

doi:10.1117/12.3029445

13 May 2024 A novel ensemble framework based on CNN models and Swin transformer for cervical cytology image classification

Yangfan Liu, Zifeng Wang, Anjun Dai, Wenhao Gu

Author Affiliations +

Proceedings Volume 13158, Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024); 131580F (2024) https://doi.org/10.1117/12.3029445
Event: Seventh International Conference on Computer Graphics and Virtuality (ICCGV24), 2024, Hangzhou, China

Abstract

Cervical cancer is one of the leading causes of women’s death. Currently, Pap smear images testing, one of the most conventional ways to check for cervical cancer, has a misidentification rate of around 40 percent which poses serious risks. Existing approaches to sorting Pap smear images are still not at an accurate enough level to be put into practical use. In this paper, we create an ensemble network by combining three CNN networks, namely DenseNet-169, VGG-19, and Xception with a Swin transformer to perform cervical cytolopy image classification on the standardized SIPaKMeD dataset and Mendeley LBC dataset. The proposed framework obtains an accuracy of 95.50% on the SIPaKMeD dataset and 98.65% on the Mendeley LBC dataset, which outperforms a majority of methods proposed on cervical cytology classification.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yangfan Liu, Zifeng Wang, Anjun Dai, and Wenhao Gu "A novel ensemble framework based on CNN models and Swin transformer for cervical cytology image classification", Proc. SPIE 13158, Seventh International Conference on Computer Graphics and Virtuality (ICCGV 2024), 131580F (13 May 2024); https://doi.org/10.1117/12.3029445

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KEYWORDS

Transformers

Data modeling

Image classification

Machine learning

Cell biology

Performance modeling

Windows

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