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ConvNet-CA: A Lightweight Attention-Based CNN for Brain Disease Detection

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Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications (IWINAC 2022)

Abstract

Attention-based convolutional networks have attracted great interest in recent years and achieved great success in improving representation capability of networks. However, most attention mechanisms are complicated and implemented by introducing a large number of extra parameters. In this study, we proposed a lightweight attention-based convolutional network (ConvNet-CA) that has a low computation complexity yet a high performance for brain disease detection. ConvNet-CA weights the importance of different channels in features maps and pays more attention to important channels by introducing an efficient channel attention mechanism. We evaluated ConvNet-CA on a publicly accessible benchmark dataset: Whole Brain Atlas. The brain diseases involved in this study are stroke, neoplastic disease, degenerative disease, and infectious disease. The experimental results showed that ConvNet-CA achieved highly competitive performance over state-of-the-art methods on distinguishing different types of brain diseases, with an overall multi-class classification accuracy of 94.88 ± 3.64%.

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Acknowledgements

This paper is partially supported by Medical Research Council Confidence in Concept Award, UK (MC_PC_17171), Royal Society International Exchanges Cost Share Award, UK (RP202G0230), British Heart Foundation Accelerator Award, UK (AA/18/3/34220), Global Challenges Research Fund (GCRF), UK (P202PF11), Sino-UK Industrial Fund, UK (RP202G0289), and Hope Foundation for Cancer Research, UK (RM60G0680).

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Correspondence to Yu-Dong Zhang or Juan M. Górriz .

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Zhu, H., Wang, J., Wang, SH., Zhang, YD., Górriz, J.M. (2022). ConvNet-CA: A Lightweight Attention-Based CNN for Brain Disease Detection. In: Ferrández Vicente, J.M., Álvarez-Sánchez, J.R., de la Paz López, F., Adeli, H. (eds) Artificial Intelligence in Neuroscience: Affective Analysis and Health Applications. IWINAC 2022. Lecture Notes in Computer Science, vol 13258. Springer, Cham. https://doi.org/10.1007/978-3-031-06242-1_1

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  • DOI: https://doi.org/10.1007/978-3-031-06242-1_1

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