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2023 | OriginalPaper | Chapter

U-Shaped Xception-Residual Network for Polyps Region Segmentation

Authors : Pallabi Sharma, Bunil Kumar Balabantary, P. Rangababu

Published in: Proceedings of International Conference on Frontiers in Computing and Systems

Publisher: Springer Nature Singapore

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Abstract

Segmenting the region of interest helps gastroenterologists for removing polyps during the surgery in the gastrointestinal tract. We propose a segmentation system to segment the area of the polyp from the informative frames. Informative frames are the frames that contain at least a polyp in colonoscopy still frames. Our proposed U-shaped convolution neural network model utilizes the concept of residual connection and Xception as a backbone structure. We consider colonoscopy still frames as input to the segmentation model and achieved a Dice and Jaccard score of 86.3 and 79, respectively. It outperformed the conventional U-net with a 3.7% performance gain with respect to Dice score. This proposed method can be used as a reliable alternative system to identify a region of polyps during colonoscopy analysis.

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Metadata
Title
U-Shaped Xception-Residual Network for Polyps Region Segmentation
Authors
Pallabi Sharma
Bunil Kumar Balabantary
P. Rangababu
Copyright Year
2023
Publisher
Springer Nature Singapore
Book
Proceedings of International Conference on Frontiers in Computing and Systems

Print ISBN: 978-981-19-0104-1

Electronic ISBN: 978-981-19-0105-8

Copyright Year: 2023

DOI
https://doi.org/10.1007/978-981-19-0105-8_25