LONG BONES X-RAY FRACTURE CLASSIFICATION USING MACHINE LEARNING

Document Type : Original Article

Authors

1 Systems and Computers Engineering Dept. , Faculty of Engineering , Al-Azhar University, Cairo, Egypt.

2 Radiology Department, Faculty of Medicine , Al-Azhar University

3 Modern 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)

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Journal of Al-Azhar University Engineering Sector
Volume 19, Issue 72
Special Issue for Al-Azhar Engineering 16th. International Conference (AEIC 2024)
July 2024
Pages 121-133

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