University of Leicester
Browse
ConvNet-CA.pdf (581.19 kB)

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

Download (581.19 kB)
conference contribution
posted on 2022-07-11, 08:08 authored by H Zhu, J Wang, SH Wang, YD Zhang, JM Górriz

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%.

Funding

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)

History

Citation

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

Author affiliation

School of Computing and Mathematical Sciences, University of Leicester

Version

  • AM (Accepted Manuscript)

Published in

Lecture Notes in Computer Science

Publisher

Springer International Publishing

issn

0302-9743

eissn

1611-3349

isbn

9783031062414

Copyright date

2022

Available date

2023-05-24

Book series

13258 LNCS

Language

en

Usage metrics

    University of Leicester Publications

    Categories

    Exports

    RefWorks
    BibTeX
    Ref. manager
    Endnote
    DataCite
    NLM
    DC