Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Deep Learning-Based Bone Fracture Detection: A Comprehensive Multi-Model Comparative Analysis with Hierarchical Gradient-Weighted Attention Visualization
0
Zitationen
6
Autoren
2025
Jahr
Abstract
Accurate bone fracture detection from radiographic images is crucial for clinical diagnosis, yet manual interpretation remains time-consuming and error-prone. We present a comprehensive deep learning framework evaluating five state-of-the-art architectures (ResNet50, EfficientNet-B3, DenseNet121, ViT-B/16, and Ensemble) on 10,580 X-ray images. Our ensemble model achieves superior performance with 96.84% accuracy, 97.15% precision, and 0.9912 AUC, while EfficientNet-B3 provides optimal accuracy-efficiency balance (96.24% accuracy, 81.3 images/s throughput). Multi-layer Grad-CAM visualization confirms models focus on anatomically relevant fracture regions, demonstrating clinical interpretability. Experimental results validate deep learning's efficacy for automated fracture screening.
Ähnliche Arbeiten
Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI
2019 · 8.245 Zit.
Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead
2019 · 8.100 Zit.
High-performance medicine: the convergence of human and artificial intelligence
2018 · 7.466 Zit.
Proceedings of the 19th International Joint Conference on Artificial Intelligence
2005 · 5.776 Zit.
Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)
2018 · 5.429 Zit.