Deep Learning and Federated Learning in Breast Cancer Screening and Diagnosis: A Systematic Review
Breast cancer remains one of the leading causes of death among women worldwide. Early detection and diagnosis are crucial for effective treatment and improved patient outcome. In recent years, both deep learning and federated learning have demonstrated significant advancements in terms of their abil...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10963696/ |
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| Summary: | Breast cancer remains one of the leading causes of death among women worldwide. Early detection and diagnosis are crucial for effective treatment and improved patient outcome. In recent years, both deep learning and federated learning have demonstrated significant advancements in terms of their ability to reliably and securely diagnose breast cancer using various imaging techniques. Consequently, a thorough summary of recent advancements in the field of breast cancer screening and diagnosis using deep learning and federated learning, along with future research directions, is imperative. This study presents a comprehensive review of recent deep learning and federated learning-based methods for breast cancer screening and diagnosis from the classification, segmentation, and detection viewpoints using ultrasonography, mammography, magnetic resonance imaging, and histopathology images. In addition, novel deep learning and federated learning architectures that are used to reliably and securely diagnose breast cancer using various imaging techniques are highlighted. Finally, a discussion of the associated challenges and future research directions are presented. |
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| ISSN: | 2169-3536 |