Enhancing Bone Fracture Detection in Radiology: A Machine Learning and Explainable AI Approach

Abstract

Bone fractures, caused by impacts, trauma or conditions such as osteoporosis, require timely and precise detection for effective medical care. Traditional approaches rely on radiologists' interpretations of X-rays, which are labor-intensive and prone to errors. Machine learning (ML) presents a promising solution for enhancing fracture identification. However, the opacity of ML models presents challenges in their integration into healthcare, necessitating transparency and interpretability. This work examines the use of ML in fracture detection and incorporates Explainable AI (XAI) techniques to clarify decision-making processes and bolster model reliability. Here, Local Directional Pattern (LDP) and Local Binary Pattern (LBP) have been used for extracting the features and it is found that the Random Forest Classifier achieved the highest accuracy of 95 %. By assessing both the accuracy and interpretability of ML models in identifying fractures, this study aims to promote wider acceptance and application of AI-driven diagnostic tools in medical settings.

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