Paper
23 May 2023 Image segmentation of gastroscopy based on ConvNeXt 2.5D UNet
Xing Dong, Hui Kang, Tian Bo, Hu Ruohan, Xiaonan Wang
Author Affiliations +
Proceedings Volume 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023); 126451W (2023) https://doi.org/10.1117/12.2681719
Event: International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 2023, Hangzhou, China
Abstract
Image segmentation as a crucial step in image processing and analysis, has important applications in video surveillance, medical detection and wafer detection, etc. Accurate and efficient image segmentation can bring great advantages and convenience to the realization of related tasks in these fields. In this paper, a 2.5D UNet network based on ConvNeXt is proposed to realize the image segmentation task based on the gastroscopy image dataset. The experimental results show that the proposed method has better segmentation performance than the UNet model based on ResNet50, UNet model based on EfficientNetB0, and UNET2.5D model based on EfficientNetB1.
© (2023) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xing Dong, Hui Kang, Tian Bo, Hu Ruohan, and Xiaonan Wang "Image segmentation of gastroscopy based on ConvNeXt 2.5D UNet", Proc. SPIE 12645, International Conference on Computer, Artificial Intelligence, and Control Engineering (CAICE 2023), 126451W (23 May 2023); https://doi.org/10.1117/12.2681719
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KEYWORDS
Image segmentation

Education and training

Medical imaging

Performance modeling

3D modeling

Data modeling

Image processing

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