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An automated segmentation model based on CBAM for MR image of glioma tumors

Published: 31 May 2022 Publication History

Abstract

As a serious disease endangering human life, the incidence of glioma is increasing in recent years. A semantic segmentation model of glioma based on the deep separable convolution of the attention mechanism is proposed. The model uses an encoder-decoder structure, where the encoder part uses an improved Xception backbone network. In the improved Xception backbone network, CBAM is added after each convolutional layer, thereby improving the segmentation accuracy. In the entire network structure, the Mish activation function is used instead of the ReLU activation function to ensure a smooth gradient descent during training and optimize network performance. The segmentation results of magnetic resonance image slices obtained based on the BraTS2019 data set show that the joint intersection is 83.68%, the Kappa coefficient is 90.74%, and the Dice coefficient is 0.9111, which is better than mainstream semantic segmentation models. The semantic segmentation model proposed in this paper has a high accuracy rate for glioma segmentation. This work can effectively alleviate the complex recognition work of doctors on tumors, and is of practical significance to the medical diagnosis process.

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BIC '22: Proceedings of the 2022 2nd International Conference on Bioinformatics and Intelligent Computing
January 2022
551 pages
ISBN:9781450395755
DOI:10.1145/3523286
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

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Published: 31 May 2022

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