Enhanced Bone Fracture Diagnosis in X-rays Using Fine-Tuned DenseNet169 Deep Learning Model

The classification of bone fractures from radiographs is an important yet challenging task in clinical diagnosis. Diagnosing fractures through X-rays remains difficult for orthopedic specialists due to image quality issues, which can result in errors, misalignments, and potential harm to patients. However, recent advancements in artificial intelligence (AI) and deep learning have revolutionized medical imaging, with state-of-the-art methods now capable of handling 2D and 3D images. This study focuses on deep-learning approaches for the classification and detection of bone fractures in radiograph images and aims to analyze and compare various deep-learning algorithms and techniques used in fracture detection. It also highlights current cutting-edge approaches in this field, providing insights and guidance for future research and practical applications. In this paper, the application of Fine-tuned DenseNet169 for the automated classification of bone fractures in X-ray images is explored. By using deep learning approaches, our method seeks to enhance the accuracy and efficiency of fracture detection. We trained and evaluated the DenseNet169 model on the MURA Stanford dataset and achieved 83% accuracy in distinguishing fractured and non-fractured elbow bones. The model’s performance highlights the potential of DenseNet169 to assist radiologists in clinical settings, promoting better patient outcomes through prompt and reliable fracture diagnosis.