Abstract
Colorectal polyp differentiation is an important clinical assessment to avoid colorectal cancer (CRC). To aid in proper diagnosis from colonoscopy images, a deep learning-based computer-aided analysis system is necessary. This paper first explains why the narrow band imaging technique is a better alternative to conventional white light images for the learning of this system. This is followed by exploring the concept of image-to-image translation using Cycle GAN which was used to acquire narrow band image data distribution via unsupervised learning. This was required because not all colonoscopy equipments are enabled with special optical enhancement tools to return NBIs, and we need more image data from this domain. The paper concludes with a set of experiments that have different combinations of the datasets acquired, to check how and which models should be trained to return the highest classification accuracy.
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Acknowledgments
Iwahori’s research is supported by Japan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research (C) (20K11873) and by Chubu University Grant.
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Bhamre, N.V., Sharma, V., Iwahori, Y., Bhuyan, M.K., Kasugai, K. (2023). Colonoscopy Polyp Classification Adding Generated Narrow Band Imaging. In: Gupta, D., Bhurchandi, K., Murala, S., Raman, B., Kumar, S. (eds) Computer Vision and Image Processing. CVIP 2022. Communications in Computer and Information Science, vol 1777. Springer, Cham. https://doi.org/10.1007/978-3-031-31417-9_25
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DOI: https://doi.org/10.1007/978-3-031-31417-9_25
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