Abstract
In the past years, semantic segmentation method based on deep learning, especially Unet, have achieved tremendous success in medical image processing field. However, due to the limitation of traditional convolution operations, Unet cannot realize global semantic information interaction. To address this problem, this paper proposes a deep learning model based on Unet. The proposed model takes the Residual network as the image feature extraction layer to alleviate the problem of gradient degradation and obtain more effective features. Besides, we add Squeeze-and-Excitation block to the encoder layer, which helps the whole network get the importance of each feature channel, and then improve the useful features and suppress the less useful features according to the importance, so as to improve the segmentation accuracy. According to the experiments on two medical image datasets, our method achieved better segmentation performance than other deep learning-based algorithms, which verified the effectiveness and efficiency of our method.
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This paper was supported by the National Natural Science Foundation of China Project No. 61863013.
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Liu, S., Wu, F., Tang, J., Li, B. (2023). A Medical Image Segmentation Method Based on Residual Network and Channel Attention Mechanism. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_5
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