Enhancing Computer-assisted Bone Fractures Diagnosis in Musculoskeletal Radiographs Based on Generative Adversarial Networks

Abstract

Computer-Assisted Bone Fractures Diagnosis in musculoskeletal radiographs plays a crucial role in aiding medical professionals in accurate and timely fracture detection. In this work, we explore a Generative Adversarial Network based approach for this task, which is a powerful deep learning model capable of generating realistic images and detecting anomalies. Our proposed approach leverages the potential of GANs to generate synthetic radiographs with fractures and identify anomalous patterns, thereby enhancing fracture diagnosis. Through extensive experimentation and evaluation on musculoskeletal radiograph datasets (MURA), we demonstrate the effectiveness of GAN-based models in improving fracture detection performance by adopting several evaluation metrics notably accuracy, precision, F1-score and detection speed. These findings highlight the potential of integrating GANs into computer-assisted diagnosis, contributing to the advancement of fracture diagnosis methodologies in orthopedics. It is important to note that GANs operate by training a generator network to produce synthetic images and a discriminator network to distinguish between real and generated images. This adversarial process fosters the generation of realistic radiographs with fractures, enabling accurate and automated detection. Our findings contribute to the advancement of fracture diagnosis methodologies and pave the way for more efficient and precise diagnostic tools in the field of orthopedics.

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