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Title |
Bone Fracture Classification Using Neural Networks from X-ray Images |
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Author |
Amal Alsahrani, Alaa Alsairafi, Athary Alsowat, Abrar Saigh, Aldanh Almatarfy, Rouaa Redwan |
| Citation |
Vol. 25 No. 6 pp. 53-60 |
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Abstract |
In this study, a bone fracture classification system using deep learning algorithms was developed to determine the best-performing architecture. The primary focus was on training the YOLOv8 model, renowned for its real-time object detection and image segmentation capabilities, as well as the VGG16 model. The CNN architecture, known for its effectiveness in image recognition tasks, was chosen for its proven effectiveness in detecting bone fractures from X-ray images. These efforts in model development and hyperparameter tuning significantly enhanced the system's ability to accurately detect and classify bone fractures. The study utilized the FracAtlas dataset, which contains 4,083 X-ray images of fractured and non-fractured human bones, to improve the accuracy and efficiency of fracture detection compared to current methods. By integrating advanced deep learning techniques, the goal was to assist surgeons with more accurate diagnostics. The performance of the developed system was evaluated against existing methodologies, showcasing its effectiveness in medical diagnostics and fracture treatment. The methodology employed, including data augmentation, extensive model training, and hyperparameter tuning, significantly improved the accuracy of bone fracture detection and classification, demonstrating the potential of deep learning models in aiding medical professionals with more precise and efficient diagnostics. |
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Keywords |
Bone fracture, Classification, Deep Learning, VGG16, YOLOV8, CNN. |
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