Lightweight medical image segmentation network based on ghost convolution and attention mechanism

Haoran Wang; Kun Yu; Qiangqiang Li; Qianjun Guan; Shihong Gao

doi:10.1117/12.2683428

26 June 2023 Lightweight medical image segmentation network based on ghost convolution and attention mechanism

Haoran Wang, Kun Yu, Qiangqiang Li, Qianjun Guan, Shihong Gao

Proceedings Volume 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023); 127210G (2023) https://doi.org/10.1117/12.2683428
Event: 2023 2nd International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 2023, Chengdu, China

Abstract

Ghostnet is a lightweight image classification network proposed by Kai Han and other scholars in 2019 which was applied in terms of object detection. Ghostnet has the advantages of few parameters and high accuracy. In this paper, through the idea of transfer learning, ghost net is used as the network of feature extraction and CBAM attention mechanism is added. CBAM is not only a lightweight attention mechanism, but also a good combination of space and channels. CBAM eliminate irrelevant noise intelligences and concern to key intelligences. At the same time, the ghost convolution block is improved, and point-by point convolution is added to the ghost convolution block to obtain more image feature information. Add the squeeze and enumeration module to improve the convolution receptive field, increase the ability of the network to extract multi-scale spatial information, and introduce the residual idea to tackle the problem of information loss and gradient descent disappearance. GAUnet, designed according to the above idea, achieves better intersection and merger ratio than Unet, Unet++, DeepLabV3+and other neural networks on the multi-organ segmented chaos dataset with fewer parameters.

Citation Download Citation

Haoran Wang, Kun Yu, Qiangqiang Li, Qianjun Guan, and Shihong Gao "Lightweight medical image segmentation network based on ghost convolution and attention mechanism", Proc. SPIE 12721, Second International Symposium on Computer Applications and Information Systems (ISCAIS 2023), 127210G (26 June 2023); https://doi.org/10.1117/12.2683428

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KEYWORDS

Convolution

Image segmentation

Neural networks

Medical imaging

Feature extraction

Image classification

Liver

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