Polyp image segmentation based on HarDNetV2 and attention mechanism

Long Yang; Wencheng Cui

doi:10.1117/12.3034775

11 July 2024 Polyp image segmentation based on HarDNetV2 and attention mechanism

Long Yang, Wencheng Cui

Proceedings Volume 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024); 132101K (2024) https://doi.org/10.1117/12.3034775
Event: Third International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2024, Wuhan, China

Abstract

Segmentation of colorectal polyps is essential for colorectal cancer prevention. The accuracy of segmentation is affected by the diversity of shapes, sizes, and textures of colorectal polyps, as well as unclear polyp boundaries. Therefore, we propose a joint HarDNetV2 and attention mechanism for polyp image segmentation network. The encoder uses HarDNetV2 to extract backbone features to improve inference speed and computational efficiency. The decoder includes an attention gate module (AG) and a convolutional attention module (CAM), in which the AG module fuses the extracted features of different sizes, the CAM module groups features into pixels and uses channel and spatial attention to suppress background information, which enhances the recognition of polyp regions and improves segmentation performance. Experimental results show that the proposed method achieves 92.17% and 88.16% performance for mDice and mIOU on the Kvasir-SEG dataset, respectively.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Long Yang and Wencheng Cui "Polyp image segmentation based on HarDNetV2 and attention mechanism", Proc. SPIE 13210, Third International Symposium on Computer Applications and Information Systems (ISCAIS 2024), 132101K (11 July 2024); https://doi.org/10.1117/12.3034775

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KEYWORDS

Polyps

Image segmentation

Content addressable memory

Silver

Colorectal cancer

Data modeling

Network architectures

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