Convolutional Neural Network Model for Bone Fracture Detection and Classification in X-Ray Images

Abstract

Bone fractures are one of the most common medical conditions worldwide. Proper and rapid diagnosis of fractures is essential to ensure effective treatment and reduce the risk of further complications. This study uses a Convolutional Neural Network (CNN) for fracture classification on X-ray images, which aims for the clinical implementation of CNN models in supporting the diagnostic process in the orthopedic field to minimize misdiagnosis due to human error. The analysis results show that fracture classification using CNN has accuracy, precision, recall, and F1-score reaching 99%, indicating highly accurate classification performance. This research aligns with the 3rd SDG's goal of good health and well-being: to ensure a healthy life and support wellbeing. The results of this research are expected to significantly contribute to the medical world, especially in improving the accuracy and efficiency of fracture diagnosis and become a foundation for developing more innovative diagnostic technologies to support more equitable and quality
health services globally.