VGG-16, VGG-16 With Random Forest, Resnet50 With SVM, and EfficientNetB0 With XGBoost-Enhancing Bone Fracture Classification in X-Ray Using Deep Learning Models
Millions of cases of bone fractures are reported every year, and accuracy in classification is crucial to help with proper management and treatment. The recently developed techniques of Machine Learning, particularly Deep Learning, have been effective in increasing diagnosis precision and efficiency...
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| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10855403/ |
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| Summary: | Millions of cases of bone fractures are reported every year, and accuracy in classification is crucial to help with proper management and treatment. The recently developed techniques of Machine Learning, particularly Deep Learning, have been effective in increasing diagnosis precision and efficiency. We utilized a diverse dataset comprising 10 different classes of fracture types captured in X-Ray images. This paper makes a comparison of different machine learning models on classifying bone fractures: VGG-16, VGG-16 with Random Forest, ResNet-50 with Support Vector Machine, and EfficientNetB0 with XGBoost. Model performances were evaluated with respect to parameters of precision, recalls, and F1-scores. According to results, VGG-16 and its variant ensemble with Random Forest outperformed with an accuracy of 0.95 when compared to others on every parameter for different classes of fractures. Results indicate that models based on VGG16 are quite effective for bone fracture classification. |
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| ISSN: | 2169-3536 |