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John L. Dart Library
Closed for Maintenance
Phone: (843) 722-7550
West Ashley Library
9 a.m. - 5 p.m.
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Folly Beach Library
9 a.m. - 2 p.m.
*open the 2nd and 4th Saturday
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Wando Mount Pleasant Library
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MSA-UNet: A Multiscale Lightweight U-Net Lung CT Image Segmentation Algorithm Under Attention Mechanism.
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- Author(s): Wang, Chuantao1,2 (AUTHOR) ; Shao, Shuo1 (AUTHOR) ; Yin, Jiajun3 (AUTHOR) ; Wang, Xiumin1 (AUTHOR) ; Li, Baoxia1 (AUTHOR)
- Source:
International Journal on Artificial Intelligence Tools. Mar2024, Vol. 33 Issue 2, p1-21. 21p.- Subject Terms:
- Source:
- Additional Information
- Abstract: Automatic and precise segmentation of lung images can assist doctors in locating and diagnosing lung lesions. However, current traditional lung CT image lesion segmentation algorithms suffer from the problem of low segmentation accuracy, while deep learning-based segmentation algorithms struggle to strike a better balance between lightweight and high accuracy. In response to this issue, a multi-scale and lightweight U-Net lung image segmentation algorithm with an attention mechanism is proposed. This algorithm introduces CA convolution after the convolution in the encoding stage to extract channel relationships and positional information from the feature maps. Furthermore, the RFB module is employed to extract features from different perspectives. Lastly, upward residual connections are introduced between the RFB modules in the encoder and decoder to enhance inter-network information interaction. Experiments conducted on the LUNA (lung nodule analysis) dataset and the COVID-QU-Ex dataset for COVID-19 pneumonia demonstrate that the proposed MSA-UNet algorithm achieves the best results in terms of Precision and Dice metrics. It outperforms mainstream models such as U-Net++ and DeeplabV3+ in terms of segmentation effectiveness and segmentation generality. The model has a floating-point operation count (FLOPs) of 18.15 G, a network parameter counts of 8.83×106, and achieves a Precision of 99.37%. The algorithm achieves a good balance between computational efficiency, model size, and segmentation accuracy. [ABSTRACT FROM AUTHOR]
- Abstract: Copyright of International Journal on Artificial Intelligence Tools is the property of World Scientific Publishing Company and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
- Abstract:
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