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An efficient federated learning method based on enhanced classification-GAN for medical image classification

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Abstract

The scarcity of medical images significantly hampers the advancement of artificial intelligence techniques in the medical field. Medical images face the issue of inferior training accuracy and efficiency in classification tasks due to insufficient labeled data and privacy preserving demands. To address these issues, we propose a new and efficient federated learning (FL) scheme based on blockchain, called FedBG, to generate realistic images to expand the dataset for medical image classification. First, we design an enhanced classification-GAN (EC-GAN) algorithm based on cross-entropy, which expands the training target of the generator from a single improvement of image fidelity to a direction related to the classification tasks. EC-GAN increases the diversity of generated images and reduces the risk of pattern collapse. Second, we improve the generator’s loss function, which is guided by the cross-entropy loss term of the classifier model and the gradient information from the discriminator model, to generate medical images with enhanced category features. This design overcomes the limitation of traditional GAN that focuses only on image authenticity judgment, and achieves the dual optimization of synthetic image and classification accuracy. Finally, we propose an improved consensus mechanism based on maximum mean discrepancy and data contribution to ensure the consistency and security of data in the blockchain. The mechanism optimizes the fairness and efficiency of the model training by dynamically evaluating the contribution of each participating node, and guarantees the trust system of FL. Experiments are performed on two datasets and results demonstrate that FedBG reduces the training time by 27–38% and improves the accuracy by 0.9–2% compared to existing methods while ensuring data privacy and generating high-quality images.

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Data availibility

The data set that support the findings of this study are openly available in: www.kaggle.com/datasets/tawsifurrahman/covid19-radiography-database?select=COVID-19_Radiography_Dataset.

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Funding

This research was funded by the Henan Key Laboratory of Network Cryptography Technology (Grant No.LNCT2022-A04) and the Key Scientific Research Project of Colleges and Universities in Henan Province NO.24A520045.

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Authors and Affiliations

  1. School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450000, China

    Wei Liu, Yurong Zheng, Yingmeng Wang, Zhao Tian & Wei She

  2. Henan Key Laboratory of Network Cryptography Technology, Zhengzhou, 450000, China

    Wei Liu

  3. Zhengzhou Key Laboratory of Blockchain and Data Intelligence, Zhengzhou, 450000, China

    Wei Liu, Zhao Tian & Wei She

  4. Rehabilitation Department of Changde First People’s Hospital, Changde, 415000, China

    Zhihui Xiang

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  1. Wei Liu
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  2. Yurong Zheng
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  4. Yingmeng Wang
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  5. Zhao Tian
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Contributions

Wei Liu: methodology, conceptualization, funding acquisition, project administration, resources, supervision, writing—review and editing. Yurong Zheng: investigation, methodology, software, validation, visualization, writing—original draft, writing—review and editing, data curation. Zhihui Xiang: data curation, resources, validation. Yingmeng Wang: investigation, software, data curation. Zhao Tian: Data curation, formal analysis, investigation. Wei She: conceptualization, resources, supervision, formal analysis.

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Correspondence to Wei She.

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Liu, W., Zheng, Y., Xiang, Z. et al. An efficient federated learning method based on enhanced classification-GAN for medical image classification. Multimedia Systems 31, 15 (2025). https://doi.org/10.1007/s00530-024-01564-w

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