A Deep Learning Approach for Binary Classification of Bone Fractures in Multi-Region X-ray Images

Abstract

Medical practice often provides bone fractures as the most consistent injuries therefore appropriate diagnosis must occur immediately to avert extended medical complications. Radiological diagnosis of X-ray images via trained radiologists remains effective but becomes slowed with addition of human error especially during peak patient exam hours. The research creates an automatic bone fracture detection system through the use of the Convolutional Neural Network (CNN) deep learning architecture which is excellent for image classification tasks. The data for the research was gathered from Kaggle's open-source database consisting of 10,580 X-rays that are either fractured or not fractured versions. The suggested CNN model functioned across the various subsets recording a 96% overall classification accuracy on training as well as evaluation. Both diagnosis classes performed strongly with accurate assessments so the model obtained 0.96 values for F1-score, recall, and precision which confirmed its generalized and reliable detection capability. The study validates that models developed on CNN technology offer great assistance to doctors for timely and accurate bone fracture detection that enables informed patient treatment choices.

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