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

Zhiwen Jiang, Ziji Ma*, Yaonan Wang, Xun Shao, Keping Yu, Alireza Jolfaei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


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.

Original languageEnglish
Pages (from-to)14653-14665
Number of pages13
JournalNeural Computing and Applications
Issue number20
Publication statusPublished - 2023 Jul
Externally publishedYes


  • Classification of medical images
  • Deep convolution neural network
  • Down-sampling
  • ResNet

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence


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