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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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10855403/ |
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| author | Spoorthy Torne Dasharathraj K. Shetty Krishnamoorthi Makkithaya Prasiddh Hegde Manu Sudhi Phani Kumar Pullela T Tamil Eniyan Ritesh Kamath Staissy Salu Pranav Bhat S. Girisha P. S. Priya |
| author_facet | Spoorthy Torne Dasharathraj K. Shetty Krishnamoorthi Makkithaya Prasiddh Hegde Manu Sudhi Phani Kumar Pullela T Tamil Eniyan Ritesh Kamath Staissy Salu Pranav Bhat S. Girisha P. S. Priya |
| author_sort | Spoorthy Torne |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f68ef8a37f014e1f8b6e190963bfc6be |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-f68ef8a37f014e1f8b6e190963bfc6be2025-02-12T00:02:13ZengIEEEIEEE Access2169-35362025-01-0113255682557710.1109/ACCESS.2025.353481810855403VGG-16, VGG-16 With Random Forest, Resnet50 With SVM, and EfficientNetB0 With XGBoost-Enhancing Bone Fracture Classification in X-Ray Using Deep Learning ModelsSpoorthy Torne0https://orcid.org/0009-0003-3386-2782Dasharathraj K. Shetty1https://orcid.org/0000-0002-5021-4029Krishnamoorthi Makkithaya2https://orcid.org/0000-0002-7919-8886Prasiddh Hegde3Manu Sudhi4https://orcid.org/0000-0003-4149-5022Phani Kumar Pullela5T Tamil Eniyan6https://orcid.org/0009-0003-3236-8408Ritesh Kamath7https://orcid.org/0009-0003-7864-5298Staissy Salu8Pranav Bhat9S. Girisha10https://orcid.org/0000-0003-2582-9600P. S. Priya11https://orcid.org/0000-0002-7201-5733School of Computer Science and Engineering (SOCSE), RV University, Bengaluru, IndiaDepartment of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Computer Science and Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Orthopedics, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Emergency Medicine, Kasturba Medical College, Manipal, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaSchool of Computer Science and Engineering (SOCSE), RV University, Bengaluru, IndiaDepartment of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok, Sikkim, IndiaDepartment of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaSchool of Computer Science and Engineering (SOCSE), RV University, Bengaluru, IndiaDepartment of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaDepartment of Radio Diagnosis, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, Karnataka, IndiaMillions 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.https://ieeexplore.ieee.org/document/10855403/Bone fractureensemble learning modelsfracture classificationmachine learningmedical imaging diagnosticsrandom forest |
| spellingShingle | Spoorthy Torne Dasharathraj K. Shetty Krishnamoorthi Makkithaya Prasiddh Hegde Manu Sudhi Phani Kumar Pullela T Tamil Eniyan Ritesh Kamath Staissy Salu Pranav Bhat S. Girisha P. S. Priya 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 IEEE Access Bone fracture ensemble learning models fracture classification machine learning medical imaging diagnostics random forest |
| title | 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 |
| title_full | 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 |
| title_fullStr | 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 |
| title_full_unstemmed | 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 |
| title_short | 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 |
| title_sort | 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 |
| topic | Bone fracture ensemble learning models fracture classification machine learning medical imaging diagnostics random forest |
| url | https://ieeexplore.ieee.org/document/10855403/ |
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