Abstract
Colorectal cancer (CRC) screening is carried out to appropriately identify and categorize the CRC precursors, called polyps. It is a crucial task for an effective treatment as unnecessary surgeries and ignorance of any potential cancer could be the situations of concern. Thus, classifying any detected lesion as an adenoma (with the potential to become cancerous) or hyperplastic (non-cancerous) is necessary. In recent years, such classification tasks are assisted by automated systems with promising outcomes. However, the extensive data needed for these systems to function satisfactorily have limited the widespread application of deep learning techniques in the medical field. To overcome this issue, in this paper, we employed a semi-supervised learning approach using GAN to classify polyps. Our approach needs limited labeled data while achieving enhanced classification performance. The experimental results show that the suggested semi-GAN approach, when applied to the PolypsSet and SUN datasets, produced a classification accuracy of 72.85% and 74.12% respectively. This represents a significant improvement over existing methods and highlights the potential of GANs in the medical image analysis field. These research findings have implications for improving the efficiency and accuracy of CRC screening and diagnosis, potentially leading to better patient outcomes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bhamre, N.V., Sharma, V., Iwahori, Y., Bhuyan, M., Kasugai, K.: Colonoscopy polyp classification adding generated narrow band imaging. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds.) International Conference on Computer Vision and Image Processing, pp. 322–334. Springer, Heidelberg (2022). https://doi.org/10.1007/978-3-031-31417-9_25
Bora, K., Bhuyan, M., Kasugai, K., Mallik, S., Zhao, Z.: Computational learning of features for automated colonic polyp classification. Sci. Rep. 11(1), 1–16 (2021)
Ciompi, F., et al.: Towards automatic pulmonary nodule management in lung cancer screening with deep learning. Sci. Rep. 7(1), 46479 (2017)
Dai, Z., Yang, Z., Yang, F., Cohen, W.W., Salakhutdinov, R.R.: Good semi-supervised learning that requires a bad gan. Adv. Neural Inf. Process. Syst. 30 (2017)
Häfner, M., Tamaki, T., Tanaka, S., Uhl, A., Wimmer, G., Yoshida, S.: Local fractal dimension based approaches for colonic polyp classification. Med. Image Anal. 26(1), 92–107 (2015)
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning. Image Recogn. 7 (2015)
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)
Kingma, D.P., Mohamed, S., Jimenez Rezende, D., Welling, M.: Semi-supervised learning with deep generative models. Adv. Neural Inf. Proces. Syst. 27 (2014)
Liu, Q., Yu, L., Luo, L., Dou, Q., Heng, P.A.: Semi-supervised medical image classification with relation-driven self-ensembling model. IEEE Trans. Med. Imaging 39(11), 3429–3440 (2020)
Misawa, M., et al.: Development of a computer-aided detection system for colonoscopy and a publicly accessible large colonoscopy video database (with video). Gastrointest. Endosc. 93(4), 960–967 (2021)
Odena, A.: Semi-supervised learning with generative adversarial networks. arXiv preprint arXiv:1606.01583 (2016)
Patel, K., et al.: A comparative study on polyp classification using convolutional neural networks. PLoS ONE 15(7), e0236452 (2020)
Ribeiro, E., Uhl, A., Wimmer, G., Häfner, M.: Exploring deep learning and transfer learning for colonic polyp classification. Comput. Math. Methods Med. 2016 (2016)
Sasmal, P., Bhuyan, M.K., Iwahori, Y., Kasugai, K.: Colonoscopic polyp classification using local shape and texture features. IEEE Access 9, 92629–92639 (2021)
Society, A.: Key statistics for colorectal cancer (2021)
Uhl, A., Wimmer, G., Hafner, M.: Shape and size adapted local fractal dimension for the classification of polyps in HD colonoscopy. In: 2014 IEEE International Conference on Image Processing (ICIP), pp. 2299–2303. IEEE (2014)
Wang, X., Chen, H., Xiang, H., Lin, H., Lin, X., Heng, P.A.: Deep virtual adversarial self-training with consistency regularization for semi-supervised medical image classification. Med. Image Anal. 70, 102010 (2021)
Wimmer, G., Uhl, A., Häfner, M.: A novel filterbank especially designed for the classification of colonic polyps. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2150–2155. IEEE (2016)
Wimmer, G., Uhl, A., Häfner, M.: A novel filterbank especially designed for the classification of colonic polyps. In: 2016 23rd International Conference on Pattern Recognition (ICPR), pp. 2150–2155 (2016). https://doi.org/10.1109/ICPR.2016.7899954
Yao, L., He, F., Peng, H., Wang, X., Zhou, L., Huang, X.: Improving colonoscopy polyp detection rate using semi-supervised learning. J. Shanghai Jiaotong Univ. (Sci.) 1–9 (2022)
Acknowledgements
Vanshali Sharma is supported by the INSPIRE fellowship (IF190362), DST, Govt. of India.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Verma, D., Sharma, V., Das, P.K. (2024). Semi-supervised Polyp Classification in Colonoscopy Images Using GAN. In: Kaur, H., Jakhetiya, V., Goyal, P., Khanna, P., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2023. Communications in Computer and Information Science, vol 2011. Springer, Cham. https://doi.org/10.1007/978-3-031-58535-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-031-58535-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-58534-0
Online ISBN: 978-3-031-58535-7
eBook Packages: Computer ScienceComputer Science (R0)