Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach

Breast cancer is deadly cancer causing a considerable number of fatalities among women in worldwide. To enhance patient outcomes as well as survival rates, early and accurate detection is crucial. Machine learning techniques, particularly deep learning, have demonstrated impressive success in variou...

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Veröffentlicht in:	Journal of digital imaging Jg. 37; H. 4; S. 1488 - 1504
Hauptverfasser:	S, Selvakanmani, Dharani Devi, G, V, Rekha, Jeyalakshmi, J
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Cham Springer International Publishing 01.08.2024 Springer Nature B.V
Schlagworte:	Accuracy Artificial neural networks Breast cancer Breast Neoplasms - diagnostic imaging Breast Neoplasms - pathology Classification Comparative analysis Computing time Data acquisition Datasets Deep Learning Feature extraction Federated learning Female Health care Health care facilities Humans Image acquisition Image enhancement Image Interpretation, Computer-Assisted - methods Imaging Machine Learning Magnetic Resonance Imaging Mammography Mammography - methods Medical imaging Medicine Medicine & Public Health Neural networks Neural Networks, Computer Privacy Radiology Survival Transfer learning Deep learning Data privacy Federated learning Medical imaging Transfer learning Classification Domain adaptation Privacy-preserving Breast cancer ResNet
ISSN:	2948-2933, 0897-1889, 2948-2925, 2948-2933, 1618-727X
Online-Zugang:	Volltext
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