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<p>This review highlights the efficacy of combining federated learning (FL) and transfer learning (TL) for cancer detection via image analysis. By integrating these techniques, research has shown improvements in diagnostic accuracy and efficiency. Specifically, the use of FL and TL has led to a measurable improvement in the precision of cancer diagnoses, with some studies reporting up to a 20% increase in accuracy compared to traditional methods. This synthesis of FL and TL optimizes distributed data usage while leveraging existing models to expedite learning and application in cancer detection tasks. A concrete assessment of the two methods, including their strengths and weaknesses, is presented. Moving on, their applications in cancer detection are discussed, including potential directions for the future. Finally, this article offers a thorough description of the functions of TL and FL in image-based cancer detection. The authors also make insightful suggestions for additional study in this rapidly developing area. The findings underscore the potential of these combined approaches to significantly advance medical imaging and cancer diagnosis, setting a promising direction for future research.</p>

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Federated and transfer learning for cancer detection based on image analysis

  • 期刊论文

  • 发表日期：

    2025年1月10日

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  • Amine Bechar,
  • Rafik Medjoudj,
  • Youssef Elmir,
  • Yassine Himeur,
  • Abbes Amira

摘要

This review highlights the efficacy of combining federated learning (FL) and transfer learning (TL) for cancer detection via image analysis. By integrating these techniques, research has shown improvements in diagnostic accuracy and efficiency. Specifically, the use of FL and TL has led to a measurable improvement in the precision of cancer diagnoses, with some studies reporting up to a 20% increase in accuracy compared to traditional methods. This synthesis of FL and TL optimizes distributed data usage while leveraging existing models to expedite learning and application in cancer detection tasks. A concrete assessment of the two methods, including their strengths and weaknesses, is presented. Moving on, their applications in cancer detection are discussed, including potential directions for the future. Finally, this article offers a thorough description of the functions of TL and FL in image-based cancer detection. The authors also make insightful suggestions for additional study in this rapidly developing area. The findings underscore the potential of these combined approaches to significantly advance medical imaging and cancer diagnosis, setting a promising direction for future research.