An auxiliary diagnosis system for cervical intraepithelial neoplasia based on colposcopic images_Journal of Biomedical Engineering

Authors：

 GU Xuelian ¹ , ZHANG Yizhu ¹ , XU Zhiyang ¹ , GUAN Rui ² , ZOU Renling ¹ , CHU Shengxuan ¹ , FANG Qingbin ¹

1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. Gynecology and Obstetrics, Changhai Hospital, the First Affiliated Hospital of Naval Medical University, Shanghai 200433, P. R. China;

Corresponding?author：

GU Xuelian, Email: guxuelian@usst.edu.cn

Keywords：

Deep learning; Multimodal fusion; Cervical intraepithelial neoplasia; Colposcopic images

DOI：

10.7507/1001-5515.202411017

Video：

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Cervical intraepithelial neoplasia is the primary type of cervical precancerous lesion; however, manual clinical diagnosis is prone to bias and has limited grading accuracy. To achieve precise automated grading of CIN, this paper proposes a multimodal fusion Swin Transformer model and develops a corresponding computer-aided diagnosis system. This method employs three-channel fusion of raw images, cervical mask images, and directional gradient histogram features to enhance lesion texture and location information. Within the Swin Transformer backbone, an atrous spatial pyramid pooling module channel attention module and a convolutional feature extraction module are embedded to balance global semantic and local detail features. A focal loss function is adopted to address class imbalance in the dataset and improve the model’s ability to identify difficult-to-classify samples. On a dataset of 3 915 clinical colposcopy images, the model achieved an overall accuracy of 90.01%, precision of 87.55%, recall of 86.17%, F1 score of 89.13%, outperforming baseline models such as VGG, ResNet, and Swin Transformer. The developed system integrates image quality screening, lesion identification, and three-level classification functions, providing an effective tool for the rapid and objective screening of clinical cervical precancerous lesions.

Citation： GU Xuelian, ZHANG Yizhu, XU Zhiyang, GUAN Rui, ZOU Renling, CHU Shengxuan, FANG Qingbin. An auxiliary diagnosis system for cervical intraepithelial neoplasia based on colposcopic images. Journal of Biomedical Engineering, 2026, 43(3): 496-503. doi: 10.7507/1001-5515.202411017 Copy

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1. Rowland A G, Brady J. Colposcopy and cervical intraepithelial neoplasia. Obstet Gynaecol Reprod Med, 2020, 30(5): 133-138.
2. Heinonen A, Jakobsson M, Kiviharju M, et al. Role of colposcopy after treatment for cervical intraepithelial neoplasia. Cancers, 2020, 12: 1683.
3. Mitra A, Tzafetas M, Lyons D, et al. Cervical intraepithelial neoplasia: screening and management. Br J Hosp Med, 2016, 77(8): 118-123.
4. Xue P, Ng M T A, Qiao Y. The challenges of colposcopy for cervical cancer screening in LMICs and solutions by artificial intelligence. BMC Med, 2020, 18(1): 169.
5. Gomes M, Provaggi E, Pembe A B, et al. Advancing cervical cancer prevention equity: innovations in self-sampling and digital health technologies across healthcare settings. Diagnostics, 2025, 15: 1176.
6. Dong S, Wang P, Abbas K. A survey on deep learning and its applications. Comput Sci Rev, 2021, 40: 100379.
7. Kather J N, Weis C A, Bianconi F, et al. Multi-class texture analysis in colorectal cancer histology. Sci Rep, 2016, 6(1): 27988.
8. Lundervold A S, Lundervold A. An overview of deep learning in medical imaging focusing on MRI. Z Med Phys, 2019, 29(2): 102-127.
9. Nogales A, García-Tejedor á J, Monge D, et al. A survey of deep learning models in medical therapeutic areas. Artif Intell Med, 2021, 112: 102020.
10. 趙霏霏. 基于DR胸片的肺結核計算機輔助診斷系統. 電子技術與軟件工程, 2016(5): 2.
11. 張澤文, 張才擎, 張成琪. 基于高清晰度CT圖像的孤立性肺結節計算機輔助診斷系統. 中國醫學物理學雜志, 2015(5): 669-673.
12. 張文悅, 賈子彥, 李青, 等. 基于ResNet和UNet的胃癌病理圖像診斷系統. 河北大學學報, 2024(4): 433-440.
13. 王洪洋, 張穎, 朱海波, 等. 基于肺部CT圖像的計算機輔助診斷系統構建. 中國醫學裝備, 2018(10): 15.
14. Al-Sheikh M H, Al Dandan O, Al-Shamayleh A S, et al. Multi-class deep learning architecture for classifying lung diseases from chest X-ray and CT images. Sci Rep, 2023, 13: 19373.
15. Zhang T, Luo Y, Li P, et al. Cervical precancerous lesions classification using pre-trained densely connected convolutional networks with colposcopy images. Biomed Signal Process Control, 2020, 55: 101566.
16. Miyagi Y, Takehara K, Miyake T. Application of deep learning to the classification of uterine cervical squamous epithelial lesion from colposcopy images. Mol Clin Oncol, 2019, 19(2): 1602-1610.
17. Saini S K, Bansal V, Kaur R, et al. ColpoNet for automated cervical cancer screening using colposcopy images. Mach Vis Appl, 2020, 31: 15.
18. Liu L, Wang Y, Liu X, et al. Computer-aided diagnostic system based on deep learning for classifying colposcopy images. Ann Transl Med, 2021, 9: 1045.
19. Zhang X, Zhao S G. Cervical image classification based on image segmentation preprocessing and a CapsNet network model. Int J Imaging Syst Technol, 2019, 29: 19-28.
20. Booth B B, Khare S K, Blanes-Vidal V, et al. Higher diagnostic accuracy of an AI model for colposcopy compared with conventional and digital colposcopic evaluation. J Low Genit Tract Dis, 2026, 30(2): 118-123.
21. Yan L, Wang Z, Shen X, et al. Time-lapsed colposcopy image-based segmentation of cervical lesion areas. Sci Rep, 2026, 16: 13344.
22. Moghtaderi S, Yaghoobian O, Wahid K A, et al. Endoscopic image enhancement: wavelet transform and guided filter decomposition-based fusion approach. J Imaging, 2024, 10: 28.
23. Al-Stouhi S, Reddy C K. Transfer learning for class imbalance problems with inadequate data. Knowl Inf Syst, 2016, 48: 201-228.
24. Ronneberger O, Fischer P, Brox T. U-Net: convolutional networks for biomedical image segmentation//2015 Medical Image Computing and Computer-Assisted Intervention (MICCAI). Cham: Springer, 2015: 234-241.
25. He K, Zhang X, Ren S, et al. Spatial pyramid pooling in deep convolutional networks for visual recognition//2014 European Conference on Computer Vision (ECCV). Zurich: Springer, 2014, 37: 1904-1916.
26. Chen L C, Papandreou G, Kokkinos I, et al. DeepLab: semantic image segmentation with deep convolutional nets. IEEE Trans Pattern Anal Mach Intell, 2016, 40: 834-848.
27. Hu J, Shen L, Albanie S, et al. Squeeze-and-excitation networks//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE, 2018: 7132-7141.
28. Qiu Y, Liu Y, Chen Y, et al. A2SPPNet: attentive atrous spatial pyramid pooling network for salient object detection. IEEE Trans Multimedia, 2023, 25: 1991-2006.
29. Ji J, Li S, Liao X, et al. Semantic segmentation based on spatial pyramid pooling and multilayer feature fusion. IEEE Trans Cogn Dev Syst, 2023, 15(3): 1524-1535.
30. Zemliachenko A N, Kozhemiakin, Abramov S R, et al. Prediction of compression ratio for DCT-based coders with application to remote sensing images. IEEE J Sel Top Appl Earth Obs Remote Sens, 2018, 11: 257-270.
31. Dalal N, Triggs B. Histograms of oriented gradients for human detection//2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR). San Diego: IEEE, 2005: 886-893.
32. Liu Z, Lin Y, Cao Y, et al. Swin transformer: hierarchical vision transformer using shifted windows//2021 IEEE/CVF International Conference on Computer Vision (ICCV). Montreal: IEEE, 2021: 9992-10002.
33. Lin T Y, Goyal P, Girshick R, et al. Focal loss for dense object detection. IEEE Trans Pattern Anal Mach Intell, 2018, 42(2): 318-327.
34. Mao A, Mohri M, Zhong Y. Cross-entropy loss functions: theoretical analysis and applications. arXiv, 2023: 2307.10313.
35. Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition//3rd International Conference on Learning Representations. San Diego: ICLR, 2015: 1-14.
36. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
37. Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 5987-5995.
38. Huang G, Liu Z, Van Der Maaten L, et al. Densely connected convolutional networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu: IEEE, 2017: 2261-2269.
39. Dosovitskiy A, Beyer L, Kolesnikov A, et al. An image is worth 16×16 words: transformers for image recognition at scale//International Conference on Learning Representations. Virtual Event: ICLR, 2021.
40. IEEE. 829-2008 - IEEE Standard for Software and System Test Documentation. New York: IEEE, 2008.

Journal of Biomedical Engineering

An auxiliary diagnosis system for cervical intraepithelial neoplasia based on colposcopic images

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