Deep Unsupervised Learning for Colonoscopy Lesion Representation and Clustering

Authors

https://doi.org/10.22105/ahse.v3i2.61

Abstract

Detecting lesions in colonoscopy remains a significant challenge due to the complexity of images and the limitations of labeled datasets. This study proposes a combined unsupervised learning approach for clustering colonoscopy images, which utilizes the Bag of Words (BoW) model for extracting local features, hierarchical autoencoders for dimensionality reduction, hierarchical clustering for effective data grouping, and Deep Belief Networks (DBNs) for identifying nonlinear patterns. This method significantly enhances lesion detection, especially when labeled data is scarce. Experimental results show clustering accuracy ranging from 91.97% to 100%, with a strong silhouette score above 0.80.  Performance improves with larger vocabulary sizes and distance metrics such as Euclidean, Chebyshev, and Cosine. Integration of hierarchical autoencoders and DBNs enhances scalability and computational efficiency under limited labeled data conditions. The proposed method improves clustering quality without requiring large labeled datasets. This unsupervised hybrid framework is applied for colon disease detection using unlabeled data and integrates techniques from pattern recognition, deep learning, computer vision, and natural language processing, reducing reliance on labeled data while improving diagnostic accuracy.  

Keywords:

Small datasets, Medical image classification, Unsupervised learning, Hierarchical autoencoders, Deep belief networks, Hybrid approach, Hierarchical clustering

References

  1. [1] Chapelle, O., Scholkopf, B., & Zien Eds., A. (2009). Semi-supervised learning [Book reviews]. IEEE Transactions on Neural Networks, 20(3), 542. https://doi.org/10.1109/TNN.2009.2015974
  2. [2] Celebi, M. E., Iyatomi, H., Schaefer, G., & Stoecker, W. V. (2009). Lesion border detection in dermoscopy images. Computerized Medical Imaging and Graphics, 33(2), 148–153. https://doi.org/10.1016/j.compmedimag.2008.11.002
  3. [3] Friedman, R. J., Rigel, D. S., & Kopf, A. W. (1985). Early detection of malignant melanoma: The role of physician examination and self-examination of the skin. CA: A Cancer Journal for Clinicians, 35(3), 130–151. https://doi.org/10.3322/canjclin.35.3.130
  4. [4] Barata, C., Ruela, M., Mendonça, T., & Marques, J. S. (2014). A bag-of-features approach for the classification of melanomas in dermoscopy images: The role of color and texture descriptors. In Computer Vision Techniques for the Diagnosis of Skin Cancer (pp. 49–69). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-39608-3_3
  5. [5] Stanley, R. J., Stoecker, W. V, & Moss, R. H. (2007). A relative color approach to color discrimination for malignant melanoma detection in dermoscopy images. Skin Research and Technology, 13(1), 62–72. https://doi.org/10.1111/j.1600-0846.2007.00192.x
  6. [6] Raju, A. S. N., Gatla, R. K., Sucharitha, G., Rajababu, M., Hussain, S. J., Lakshmi, C. B. N., … ., & Abass, K. S. (2026). ColoXAI-RecomNet: Explainable recommender framework for colorectal cancer classification using integrated CNN ensemble and LIME interpretability. Journal of Imaging Informatics in Medicine. https://doi.org/10.1007/s10278-026-02007-w
  7. [7] Dong, C., & Du, G. (2026). Polyp segmentation for colonoscopy images via Hierarchical Interworking Decoding. Biomedical Signal Processing and Control, 112, 108737. https://doi.org/10.1016/j.bspc.2025.108737
  8. [8] Mori, Y., Kudo, S., Berzin, T. M., Misawa, M., & Takeda, K. (2017). Computer-aided diagnosis for colonoscopy. Endoscopy, 49(08), 813–819. https://doi.org/10.1055/s-0043-109430
  9. [9] Nishida, N., Yamakawa, M., Shiina, T., & Kudo, M. (2019). Current status and perspectives for computer-aided ultrasonic diagnosis of liver lesions using deep learning technology. Hepatology International, 13(4), 416–421. https://doi.org/10.1007/s12072-019-09937-4
  10. [10] GV, H. M. (2025). A novel approach using CapsNet and deep belief network for detection and identification of oral leukopenia. https://doi.org/10.48550/arXiv.2501.00876
  11. [11] Juliet, S. (2020). Deep medical image reconstruction with autoencoders using deep boltzmann machine training. EAI Endorsed Transactions on Pervasive Health & Technology, 6(24). https://doi.org/10.4108/eai.24-9-2020.166360
  12. [12] Qi, H., Xie, S., Chen, Y., Wang, C., Wang, T., Sun, B., & Sun, M. (2022). Prediction methods of common cancers in China using PCA-ANN and DBN-ELM-BP. IEEE Access, 10, 113397–113409. https://ieeexplore.ieee.org/abstract/document/9924251/
  13. [13] Menaga, D., & Revathi, S. (2021). Probabilistic principal component analysis (PPCA) based dimensionality reduction and deep learning for cancer classification. Intelligent computing and applications (pp. 353–368). Singapore: Springer Singapore. https://doi.org/10.1007/978-981-15-5566-4_31%0A%0A
  14. [14] Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507. https://doi.org/10.1126/science.1127647
  15. [15] Pratiwi, M., & Arsyah, U. I. (2025). Analysis of shape and texture identifying and detecting apple fruit. Innovative: Journal of Social Science Research, 5(1), 3826–3837. https://doi.org/10.31004/innovative.v5i1.17727
  16. [16] Guo, Z., Wang, Y., Shen, Y., Zhu, X., Nemoto, D., Takayanagi, D., … ., & Togashi, K. (2017). Automatic polyp recognition from colonoscopy images based on bag of visual words. 2017 IEEE 8th International Conference on Awareness Science and Technology (ICAST) (pp. 18–22). IEEE. https://doi.org/10.1109/ICAwST.2017.8256441
  17. [17] Li, H., Bai, L., Gao, W., Xie, J., & Huang, L. (2024). Many-objective coevolutionary learning algorithm with extreme learning machine auto-encoder for ensemble classifier of feedforward neural networks. Expert Systems with Applications, 246, 123186. https://doi.org/10.1016/j.eswa.2024.123186
  18. [18] Abdellatif, A., Mubarak, H., Abdellatef, H., Kanesan, J., Abdelltif, Y., Chow, C. O., … ., & Kendall, G. (2024). Computational detection and interpretation of heart disease based on conditional variational auto-encoder and stacked ensemble-learning framework. Biomedical Signal Processing and Control, 88, 105644. https://doi.org/10.1016/j.bspc.2023.105644
  19. [19] Gupta, R., Mathur, J., & Garg, V. (2025). A criteria-based climate classification approach considering clustering and building thermal performance: Case of India. Building and environment, 270, 112512. https://doi.org/10.1016/j.buildenv.2024.112512
  20. [20] Yao, Y., Peng, B., Qin, T., Gu, Y., Ling, N., & Lei, J. (2025). Hypergraph contrastive learning for large-scale hyperspectral image clustering. IEEE Transactions on Circuits and Systems for Video Technology. https://ieeexplore.ieee.org/abstract/document/10857448/
  21. [21] Hinton, G. E., Osindero, S., & Teh, Y. W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. https://ieeexplore.ieee.org/abstract/document/6796673/
  22. [22] Jain, N., Kamalraj, R., & Agrawal, A. (2024). Classifying clinically important cancers using deep belief networks. 2024 International Conference on Optimization Computing and Wireless Communication (ICOCWC) (pp. 1–6). IEEE. https://ieeexplore.ieee.org/abstract/document/10470868/
  23. [23] Boopathi Raja, G. (2025). Deep learning techniques for gene selection and cancer classification: a detailed review. In Computational intelligence for genomics data (pp. 79–96). Academic Press. https://doi.org/10.1016/B978-0-443-30080-6.00015-8

Downloads

Published

2026-06-04

Issue

Vol. 3 No. 2 (2026): Annals of Healthcare Systems Engineering

Section

Articles

How to Cite

Bayrami Rad, V., Mazinani, M., & Mirzarezaee, M. . (2026). Deep Unsupervised Learning for Colonoscopy Lesion Representation and Clustering. Annals of Healthcare Systems Engineering, 3(2), 93-101. https://doi.org/10.22105/ahse.v3i2.61

Similar Articles

11-20 of 34

You may also start an advanced similarity search for this article.