ArticleBMC medical imaging2024
Revolutionizing breast ultrasound diagnostics with EfficientNet-B7 and Explainable AI.
Article in BMC medical imaging, 2024. 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.
- A Hybrid Model for Ultrasound Image-Based Breast Cancer Diagnosis Using EfficientNet-V2 and Vision Transformer.Diagnostics (Basel, Switzerland) · 2026Article
- Radiography-based AI decision support for further post-traumatic knee MRI referral in children.BMC medical imaging · 2026Article
- Ensemble transfer learning for classifying physical examinations in GP consultation: a multi-model approach to human-object and human-to-human activity recognition.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- DANEELpath open source digital analysis tools for histopathological research in neuroblastoma models.Scientific reports · 2026Article
- A deep learning and radiomics fusion model enhances endoscopic ultrasonography diagnosis of gastric tumors.Frontiers in oncology · 2026Article
- Preliminary exploration on using entropy-weighted hybrid pooling in CNN for ultrasound breast cancer detection.Frontiers in oncology · 2026Article
- Symmetry-guided explainable deep learning for colon cancer diagnosis: model benchmarking, cross-validation, statistical analysis, and explainability via ablation studies.Frontiers in artificial intelligence · 2026Article
- Explainable AI in breast cancer ultrasound imaging: current developments and challenges.Frontiers in digital health · 2026Review
- SEED-Net: statistical echo evidence and detail modeling for ultrasound lesion classification across multiple organ-specific datasets.Frontiers in medicine · 2026Article
- Predicting biological sex in pediatric skeleton X-rays using artificial intelligence.Scientific reports · 2025Article
- Automated CT segmentation for lower extremity tissues in lymphedema evaluation using deep learning.European radiology · 2025Article
- Impact of test set composition on AI performance in pediatric wrist fracture detection in X-rays.European radiology · 2025Article
- Artificial intelligence based sonographic differentiation between skull fractures and normal sutures in young children.Scientific reports · 2025Article
- Multi-Class Classification of Breast Ultrasound Images Using Vision Transformer-Based Ensemble Learning.Diagnostics (Basel, Switzerland) · 2025Article
- MSFE-GallNet-X: a multi-scale feature extraction-based CNN Model for gallbladder disease analysis with enhanced explainability.BMC medical imaging · 2025Article
- Deep learning approaches for classification tasks in medical X-ray, MRI, and ultrasound images: a scoping review.BMC medical imaging · 2025Article
- Enhanced breast cancer diagnosis using modified InceptionNet-V3: a deep learning approach for ultrasound image classification.Frontiers in physiology · 2025Article
- Deep learning-based Alzheimer's disease detection using magnetic resonance imaging and gene expression data.PloS one · 2025Article
- Optimizing breast cancer ultrasound diagnosis: a comparative study of AI model performance and image resolution.Frontiers in oncology · 2025Article
- Population health management fit lifecycles in analytics.Frontiers in artificial intelligence · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer is a leading cause of mortality among women globally, necessitating precise classification of breast ultrasound images for early diagnosis and treatment. Traditional methods using CNN architectures such as VGG, ResNet, and DenseNet, though somewhat effective, often struggle with class imbalances and subtle texture variations, leading to reduced accuracy for minority classes such as malignant tumors. To address these issues, we propose a methodology that leverages EfficientNet-B7, a scalable CNN architecture, combined with advanced data augmentation techniques to enhance minority class representation and improve model robustness. Our approach involves fine-tuning EfficientNet-B7 on the BUSI dataset, implementing RandomHorizontalFlip, RandomRotation, and ColorJitter to balance the dataset and improve model robustness. The training process includes early stopping to prevent overfitting and optimize performance metrics. Additionally, we integrate Explainable AI (XAI) techniques, such as Grad-CAM, to enhance the interpretability and transparency of the model's predictions, providing visual and quantitative insights into the features and regions of ultrasound images influencing classification outcomes. Our model achieves a classification accuracy of 99.14%, significantly outperforming existing CNN-based approaches in breast ultrasound image classification. The incorporation of XAI techniques enhances our understanding of the model's decision-making process, thereby increasing its reliability and facilitating clinical adoption. This comprehensive framework offers a robust and interpretable tool for the early detection and diagnosis of breast cancer, advancing the capabilities of automated diagnostic systems and supporting clinical decision-making processes.
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