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Aggregated decentralized down-sampling-based ResNet for smart healthcare systems

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Abstract

With the rapid growth of the world’s population and urbanization, people are increasingly seeking higher-quality medical services to improve their lives. The classification method based on deep convolutional neural networks (CNNs) is widely used in smart healthcare systems along with advancements in communication and hardware technology. Unfortunately, for conventional deep CNN algorithms, most of the regions do not participate in the convolution operation, resulting in the loss of feature information and the correlation of information between the features. To address this issue, this paper proposes a new strategy of aggregation decentralized down-sampling to prevent the loss of feature information. The regions that are not involved in the convolution operation are re-convoluted and stacked onto depth information in the forward propagation layer and the short-circuit layer, ensuring gradual convergence of the feature map and avoiding the loss of feature information. The accuracy of the proposed residual network (ResNet) system for classification tasks showed an average improvement of 2.57% compared with the conventional ResNet strategies.

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Acknowledgements

This work was supported in part by the National Nature Science Foundation of China under Grants 61971182 and 61771191 and in part by Changsha City Science and Technology Department Funds under Grants CSKJ2019-08 and CSKJ2020-12.

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Authors and Affiliations

  1. College of Electrical and Information Engineering, Hunan University, Changsha, 410082, China

    Zhiwen Jiang, Ziji Ma & Yaonan Wang

  2. School of Regional Innovation and Social Design Engineering, Kitami Institute of Technology, Kitami , Japan

    Xun Shao

  3. Global Information and Telecommunication Institute, Waseda University, Tokyo, Japan

    Keping Yu

  4. Department of Computing, Macquarie University, Sydney, NSW, 2113, Australia

    Alireza Jolfaei

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  1. Zhiwen Jiang
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Correspondence to Ziji Ma.

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Jiang, Z., Ma, Z., Wang, Y. et al. Aggregated decentralized down-sampling-based ResNet for smart healthcare systems. Neural Comput & Applic 35, 14653–14665 (2023). https://doi.org/10.1007/s00521-021-06234-w

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