mlsanityData to 7 Oct 2026

Federated and Transfer Learning for Cancer Detection Based on Image Analysis

arXiv 2405.20126 ↗First posted 30 May 2024cs.CVcited by 3

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Abstract (arXiv)

This review article discusses the roles of federated learning (FL) and transfer learning (TL) in cancer detection based on image analysis. These two strategies powered by machine learning have drawn a lot of attention due to their potential to increase the precision and effectiveness of cancer diagnosis in light of the growing importance of machine learning techniques in cancer detection. FL enables the training of machine learning models on data distributed across multiple sites without the need for centralized data sharing, while TL allows for the transfer of knowledge from one task to another. A comprehensive 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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 99 of its 318 references that are in the map. First posted on arXiv 30 May 2024.

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99 of 318 references.

References · 99

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38 more
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Citing papers per year: 2025: 3.

3 papers.

  1. 2025Leveraging Transfer Learning and Mobile-enabled Convolutional Neural Networks for Improved Arabic Handwritten Character Recognitioncited by 3
  2. 2025Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Studycited by 1
  3. 2025A Novel Hybrid Deep Learning and Chaotic Dynamics Approach for Thyroid Cancer Classificationcited by 0