ArticleScientific reports2025
A hybrid explainable federated-based vision transformer framework for breast cancer prediction via risk factors.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Multi-Parametric Ultrasound Radiomic Kinetics with Machine Learning Ensemble for Early Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Addressing Clinical Ambiguity in Breast Density Assessment: A Hybrid Multi-View Deep Learning Framework for BI-RADS B vs. C Classification.Diagnostics (Basel, Switzerland) · 2026Article
- A Robustness-Oriented Quantum-Classical Hybrid Machine Learning Pipeline for Breast Cancer Diagnosis: External Validation, Explainability, and Rigorous Benchmarking in the NISQ Era.Diagnostics (Basel, Switzerland) · 2026Article
- Improving IoT security through an explainable hybrid CNN-transformer model and federated learning.Scientific reports · 2026Article
- Deep learning-based breast cancer detection with customized ensemble attention.Scientific reports · 2026Article
- Enhancement of cryptography algorithms for security of cloud-based IoT with machine learning models.Scientific reports · 2026Article
- Toward general AI harmonization with real mammogram imaging breast cancer dataset.BMC research notes · 2026Article
- A Data-Efficient Machine Learning Approach for Breast Ultrasound Lesion Classification Integrating Image-Derived Features and Sonographic Descriptors.Diagnostics (Basel, Switzerland) · 2026Article
- An Interpretable Ensemble Transformer Framework for Breast Cancer Detection in Ultrasound Images.Diagnostics (Basel, Switzerland) · 2026Article
- A unified multi modal transformer framework for breast cancer recurrence prediction and survival analysis.Scientific reports · 2026Article
- Integrating Large Language Models with Deep Learning for Breast Cancer Treatment Decision Support.Diagnostics (Basel, Switzerland) · 2026Article
- Predicting tourism growth in Saudi Arabia with machine learning models for vision 2030 perspective.Scientific reports · 2026Article
- Radiomics-based Machine Learning Models for Classifying Breast Cancer on Dynamic-Contrast Enhanced MRI through Multi-Observer AnalysisCurrent medical imaging · 2026Article
- Explainable Mammogram Analysis with EfficientNetV2 and Grad-CAM++ for Robust Cancer Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Classification of breast cancer using hybridization version of walrus and particle swarm optimization algorithm.Scientific reports · 2025Article
- Enhancing automatic diagnosis of thyroid nodules from ultrasound scans leveraging deep learning models.Scientific reports · 2025Article
- A hybrid vision transformer with ensemble CNN framework for cervical cancer diagnosis.BMC medical informatics and decision making · 2025Article
- BCECNN: an explainable deep ensemble architecture for accurate diagnosis of breast cancer.BMC medical informatics and decision making · 2025Article
- Integrating CNN and transformer architectures for superior Arabic printed and handwriting characters classification.Scientific reports · 2025Article
- Hierarchical Swin Transformer Ensemble with Explainable AI for Robust and Decentralized Breast Cancer Diagnosis.Bioengineering (Basel, Switzerland) · 2025Article
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7 authors.
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Abstract
Breast cancer remains a leading cause of mortality in women, underscoring the need for timely and accurate diagnosis. This paper addresses this challenge by introducing a comprehensive explainable federated learning framework for breast cancer prediction. We evaluate three deep learning approaches in both centralized and federated scenario settings: (1) individual artificial intelligence (AI) models, (2) high-level feature space ensemble models, and (3) a hybrid model combining global Vision Transformer (ViT) and local convolutional neural network (CNN) features. These models are assessed on binary, multi-class, and Breast Imaging Reporting and Data System (BI-RADS) classification tasks using a unique dataset encompassing real-world risk factors. In the federated scenario, we employ three clients with the same approaches as the centralized setting, aggregating their predictions using an AI global model. Explainable AI (XAI) technique is incorporated to enhance AI models' transparency. Our federated learning approach demonstrates superior performance, achieving accuracies of 98.65%, 97.30%, and 95.59% for binary, multi-class, and BI-RADS tasks, respectively. The proposed model, evaluated with a 95% Confidence Interval (CI) and Areas Under Curve (AUC) curves, registers top classifiers with an AUC of 0.970 [0.917-1]. Local Interpretable Model-Agnostic Explanations (LIME) XAI-based federated learning framework offers a promising solution for privacy-preserving and accurate breast cancer prediction in both research and clinical practice.
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