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CNN-Based Hidden-Layer Topological Structure Design and Optimization Methods for Image Classification

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Abstract

Convolutional neural networks (CNN) is one of the most important branches of deep learning, which always shows the excellent performance on image classification via unique convolution operations. However, the generalization ability of CNN is always limited due to lack of the specific guidelines in hidden-layer design, Kernel design and Weight initialization design. In this paper, a new topological design method is proposed by analyzing abstract edge information (called texture) in feature map based on the experimental and numerical analysis. Especially, the prior number of convolution kernels in the first layer and combinatorial optimization of all hidden layers are applied to initialize the entire network topology. The experiments based on the MNIST, Chest X-ray and CTs dataset indicate that (1) Traditional CNN layers with doubling nodes are not essential to optimize the hidden-layer topology because of the texture features that extracted from different datasets. (2) Improved hidden-layer topology of the CNN can outperform the better performance in classification-tasks and improvement up to 30% compared with the benchmark methods.

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Acknowledgements

This project is supported by the National Natural Science Foundation of China (NSFC) (Nos. 61806087, 61902158).

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Authors and Affiliations

  1. School of Computer, Jiangsu University of Science and Technology, Zhenjiang, 212003, Jiangsu, China

    Jian Liu, Haijian Shao & Xing Deng

  2. School of Automation Key Laboratory of Measurement and Control for CSE, Ministry of Education, Southeast University, Nanjing, 210096, Jiangsu, China

    Haijian Shao & Xing Deng

  3. Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV, 89154, USA

    Yingtao Jiang

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  1. Jian Liu
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  2. Haijian Shao
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  4. Xing Deng
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Correspondence to Haijian Shao.

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Liu, J., Shao, H., Jiang, Y. et al. CNN-Based Hidden-Layer Topological Structure Design and Optimization Methods for Image Classification. Neural Process Lett 54, 2831–2842 (2022). https://doi.org/10.1007/s11063-022-10742-8

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