Applied and Computational Engineering
- The Open Access Proceedings Series for Conferences
Proceedings of the 6th International Conference on Computing and Data Science
Series Vol. 69 , 21 June 2024
Open
Access | Article
Lung X-ray image segmentation based on improved Unet deep learning network algorithm with GSConv module
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Abstract
In this paper, we propose an optimised image segmentation method by structurally innovating and improving the traditional Unet model and integrating the latest GSConv module. In our experiments, we integrate the GSConv module into the encoder and decoder parts of U-Net to take advantage of its excellent feature extraction and information transfer capabilities. In comparing the training process of the two models of Unet and GSConv Unet, it is found that GSConv Unet has faster convergence speed and better generalisation ability, and finally shows higher segmentation accuracy and iou values in the test part. From the segmentation results, GSConv Unet delineates the lung region more accurately and meticulously compared to Unet, providing an effective idea for lung X-ray image segmentation tasks. This research is of great significance, which not only improves the effectiveness of the image segmentation task but also brings new technological breakthroughs in the field of medical imaging. By introducing the GSConv module and optimising the Unet structure, we have successfully improved the precision and efficiency of lung X-ray image segmentation, providing doctors with a more reliable and accurate diagnostic tool.
Keywords
Lung X-ray Image, Deep Learning Network Algorithm, GSConv
References
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Data Availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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- Volume Title
- Proceedings of the 6th International Conference on Computing and Data Science
- ISBN (Print)
- 978-1-83558-459-0
- ISBN (Online)
- 978-1-83558-460-6
- Published Date
- 21 June 2024
- Series
- Applied and Computational Engineering
- ISSN (Print)
- 2755-2721
- ISSN (Online)
- 2755-273X
- DOI
- 10.54254/2755-2721/69/20241475
- Copyright
- 21 June 2024
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited