LUneXt: Simple and Efficient U-shaped Network Design for Medical Image Segmentation with Nonlinear Activation
Medical image segmentation has always been a challenging task. This paper proposes a new LUneXt medical image segmentation model based on the characteristics analysis of medical image data sets and testing of different nonlinear activation units. Both normalized activations for the original negative...
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| Format: | Article |
| Language: | English |
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World Scientific Publishing
2024-01-01
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| Series: | Computing Open |
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| Online Access: | https://www.worldscientific.com/doi/10.1142/S2972370124500077 |
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| author | Guanghong Deng Bing Yu Wenlong Jing Yong Li Xiaodan Zhao |
| author_facet | Guanghong Deng Bing Yu Wenlong Jing Yong Li Xiaodan Zhao |
| author_sort | Guanghong Deng |
| collection | DOAJ |
| description | Medical image segmentation has always been a challenging task. This paper proposes a new LUneXt medical image segmentation model based on the characteristics analysis of medical image data sets and testing of different nonlinear activation units. Both normalized activations for the original negative input image activation have good optimization capabilities for the tokenization module parameters proposed in the original UneXt model. Using different activation coefficients for different foreground and background areas has achieved better results. The experimental results of this paper on the Breast Ultrasound Images (BUSI) data set reached an intersection over union (IoU) value of 62.64%, a Dice value of 76.12%, and a single inference speed of 807.57[Formula: see text]ms. The experimental IoU value of the International Skin Imaging Collaboration (ISIC 2018) data set reached 82.95%, and the Dice value reached 90.50%. The single inference speed reached 842.58[Formula: see text]ms. The LUneXt model is more robust than other models. While improving model performance, it does not introduce higher computational complexity and does not have a major impact on the processing speed of a single image. |
| format | Article |
| id | doaj-art-281e3815c0284aa48be5715f576143ed |
| institution | Kabale University |
| issn | 2972-3701 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | World Scientific Publishing |
| record_format | Article |
| series | Computing Open |
| spelling | doaj-art-281e3815c0284aa48be5715f576143ed2025-02-04T03:24:11ZengWorld Scientific PublishingComputing Open2972-37012024-01-010210.1142/S2972370124500077LUneXt: Simple and Efficient U-shaped Network Design for Medical Image Segmentation with Nonlinear ActivationGuanghong Deng0Bing Yu1Wenlong Jing2Yong Li3Xiaodan Zhao4GuangDong Engineering Technology Research Center of UAV Remote Sensing Network, Guangzhou iMapCloud Intelligent Technology Co., Ltd., Guangzhou 510095, P. R. ChinaGuangDong Engineering Technology Research Center of UAV Remote Sensing Network, Guangzhou iMapCloud Intelligent Technology Co., Ltd., Guangzhou 510095, P. R. ChinaGuangdong Province Engineering Laboratory for Geographic Spatiotemporal Big Data, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, P. R. ChinaGuangdong Province Engineering Laboratory for Geographic Spatiotemporal Big Data, Key Laboratory of Guangdong for Utilization of Remote Sensing and Geographical Information System, Guangdong Open Laboratory of Geospatial Information Technology and Application, Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou 510070, P. R. ChinaGuangDong Engineering Technology Research Center of UAV Remote Sensing Network, Guangzhou iMapCloud Intelligent Technology Co., Ltd., Guangzhou 510095, P. R. ChinaMedical image segmentation has always been a challenging task. This paper proposes a new LUneXt medical image segmentation model based on the characteristics analysis of medical image data sets and testing of different nonlinear activation units. Both normalized activations for the original negative input image activation have good optimization capabilities for the tokenization module parameters proposed in the original UneXt model. Using different activation coefficients for different foreground and background areas has achieved better results. The experimental results of this paper on the Breast Ultrasound Images (BUSI) data set reached an intersection over union (IoU) value of 62.64%, a Dice value of 76.12%, and a single inference speed of 807.57[Formula: see text]ms. The experimental IoU value of the International Skin Imaging Collaboration (ISIC 2018) data set reached 82.95%, and the Dice value reached 90.50%. The single inference speed reached 842.58[Formula: see text]ms. The LUneXt model is more robust than other models. While improving model performance, it does not introduce higher computational complexity and does not have a major impact on the processing speed of a single image.https://www.worldscientific.com/doi/10.1142/S2972370124500077Image segmentationmedical imagesneural networksactivation function |
| spellingShingle | Guanghong Deng Bing Yu Wenlong Jing Yong Li Xiaodan Zhao LUneXt: Simple and Efficient U-shaped Network Design for Medical Image Segmentation with Nonlinear Activation Computing Open Image segmentation medical images neural networks activation function |
| title | LUneXt: Simple and Efficient U-shaped Network Design for Medical Image Segmentation with Nonlinear Activation |
| title_full | LUneXt: Simple and Efficient U-shaped Network Design for Medical Image Segmentation with Nonlinear Activation |
| title_fullStr | LUneXt: Simple and Efficient U-shaped Network Design for Medical Image Segmentation with Nonlinear Activation |
| title_full_unstemmed | LUneXt: Simple and Efficient U-shaped Network Design for Medical Image Segmentation with Nonlinear Activation |
| title_short | LUneXt: Simple and Efficient U-shaped Network Design for Medical Image Segmentation with Nonlinear Activation |
| title_sort | lunext simple and efficient u shaped network design for medical image segmentation with nonlinear activation |
| topic | Image segmentation medical images neural networks activation function |
| url | https://www.worldscientific.com/doi/10.1142/S2972370124500077 |
| work_keys_str_mv | AT guanghongdeng lunextsimpleandefficientushapednetworkdesignformedicalimagesegmentationwithnonlinearactivation AT bingyu lunextsimpleandefficientushapednetworkdesignformedicalimagesegmentationwithnonlinearactivation AT wenlongjing lunextsimpleandefficientushapednetworkdesignformedicalimagesegmentationwithnonlinearactivation AT yongli lunextsimpleandefficientushapednetworkdesignformedicalimagesegmentationwithnonlinearactivation AT xiaodanzhao lunextsimpleandefficientushapednetworkdesignformedicalimagesegmentationwithnonlinearactivation |