LONG BONES X-RAY FRACTURE CLASSIFICATION USING MACHINE LEARNING | ||
| Journal of Al-Azhar University Engineering Sector | ||
| Volume 19, Issue 72, July 2024, Pages 121-133 PDF (1.2 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/auej.2024.259630.1577 | ||
| Authors | ||
| Soaad Nasser Eldin Ali* 1; Hala Maghraby Sherif2; Sabry Mohammed Hassan3; Ashraf Abd El Rahman El Marakby1 | ||
| 1Systems and Computers Engineering Dept. , Faculty of Engineering , Al-Azhar University, Cairo, Egypt. | ||
| 2Radiology Department, Faculty of Medicine , Al-Azhar University | ||
| 3Modern Academy of Engineering and Technology | ||
| Abstract | ||
| Accurate long bone fracture diagnosis is essential to prevent permanent deformities resulting from misdiagnosis. This study uses machine learning to introduce a multi-class classification and detection system for long bone fractures. In this study, two image classifications are applied Binary classification and Multi-class classification, and an image detection model. Binary classification to distinguish normal and fractured bone X-ray images. Three models are used for this classification, Model A and Model B are used for grayscale images, and a ResNet50 pertained model for RGB images. Multi-class classification to identify fracture type using ResNet50 fine-tuned model And a Faster RCNN detection model to classify and detect the fracture type and its location in the X-ray images. The dataset was collected from various resources and labeled and annotated following Müller AO classification for bone fracture types. Binary classification achieved a 90.2% accuracy rate for Model A, 90.85% for Model B, and 96.5% for ResNet50, While the multi-class classification model achieved 87.7% accuracy in identifying fracture types for ResNet50 and 80% for Faster RCNN in fracture detection. Special Issue of AEIC 2024 (Electrical and System & Computer Engineering Session) | ||
| Keywords | ||
| Image Classification; Image Detection; CNN; ResNet50; Faster RCNN | ||
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