Bone Fracture Detection through Advanced Neural Network Architectures

¹ Department of Artificial Intelligence and Data Science, Vardhaman College of Engineering, Hyderabad, India
² Department of Artificial Intelligence and Data Science, Vardhaman College of Engineering, Hyderabad, India
³ Department of Artificial Intelligence and Data Science, Lovely Professional University, Phagwara, India
⁴ Department of Mechanical Engineering, New Horizon College of Engineering, Bangalore, India
⁵ Department of Artificial Intelligence and Data Science, Vardhaman College of Engineering, Hyderabad, India
⁶ Department of Artificial Intelligence and Data Science, Vardhaman College of Engineering, Hyderabad, India

^* Corresponding Author: venkatesamanne@gmail.com

Abstract

Detecting bone fractures accurately is essential in radiology, as missed diagnoses can seriously affect patient health. This study introduces a deep learning approach using the InceptionV3 model, designed to identify fractures in high-resolution CT images of bones like the femur, tibia, and radius. By harnessing InceptionV3’s powerful feature extraction and applying data augmentation to reflect varied imaging conditions, the model achieved an impressive 96% accuracy, surpassing a custom CNN model’s 82.52% accuracy. This technology not only enhances the diagnostic process by reducing radiologists’ error rates but also allows them to concentrate on complex cases that need closer attention. Introducing this model into clinical settings could make fracture detection faster and more reliable, handling a high volume of cases efficiently while giving radiologists crucial support. Ultimately, this model promises to raise diagnostic accuracy, enable quicker treatment, and help deliver a higher standard of care for patients.