ArticleSensors (Basel, Switzerland)2022
Automated Breast Cancer Detection Models Based on Transfer Learning.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- Machine Learning and Deep Learning for Early Disease Detection Across Modalities: A Comprehensive Review of Methods, Applications, and Clinical Translation.Diagnostics (Basel, Switzerland) · 2026Review
- Using transfer learning approaches to predict RNA-Seq gene expression data for cancer classification.Frontiers in artificial intelligence · 2026Article
- HMC-net: a ResNet fused hierarchical multi-scale cross-attention architecture for mammographic breast malignancy recognition incorporating explainable AI.Frontiers in oncology · 2026Article
- From Machine Learning to Ensemble Approaches: A Systematic Review of Mammogram Classification Methods.Diagnostics (Basel, Switzerland) · 2025Review
- BCED-Net: Breast Cancer Ensemble Diagnosis Network using transfer learning and the XGBoost classifier with mammography images.Osong public health and research perspectives · 2024Article
- Machine learning and new insights for breast cancer diagnosis.The Journal of international medical research · 2024Review
- Deep learning empowered breast cancer diagnosis: Advancements in detection and classification.PloS one · 2024Article
- Offloading the computational complexity of transfer learning with generic features.PeerJ. Computer science · 2024Article
- Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization.Biomimetics (Basel, Switzerland) · 2023Article
- Review
- BCDiagnostics (Basel, Switzerland) · 2023Article
- Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance.Scientific reports · 2023Article
- Chimp Optimization Algorithm Influenced Type-2 Intuitionistic Fuzzy C-Means Clustering-Based Breast Cancer Detection System.Cancers · 2023Article
- Breast Cancer Detection and Classification Empowered With Transfer Learning.Frontiers in public health · 2022Article
- Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning.Computational intelligence and neuroscience · 2022Article
- Article
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Authors and funding
2 authors.
Funding
Abstract
Breast cancer is among the leading causes of mortality for females across the planet. It is essential for the well-being of women to develop early detection and diagnosis techniques. In mammography, focus has contributed to the use of deep learning (DL) models, which have been utilized by radiologists to enhance the needed processes to overcome the shortcomings of human observers. The transfer learning method is being used to distinguish malignant and benign breast cancer by fine-tuning multiple pre-trained models. In this study, we introduce a framework focused on the principle of transfer learning. In addition, a mixture of augmentation strategies were used to prevent overfitting and produce stable outcomes by increasing the number of mammographic images; including several rotation combinations, scaling, and shifting. On the Mammographic Image Analysis Society (MIAS) dataset, the proposed system was evaluated and achieved an accuracy of 89.5% using (residual network-50) ResNet50, and achieved an accuracy of 70% using the Nasnet-Mobile network. The proposed system demonstrated that pre-trained classification networks are significantly more effective and efficient, making them more acceptable for medical imaging, particularly for small training datasets.
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Registered trials
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