Osteo fracture identification using deep learning techniques.

Fractures of bone represent a prevalent medical issue in human beings. These fractures manifest as a consequence of various circumstances, most notably through accidents and incidents entailing a substantial force applied to the skeletal structure. Identifying fractures accurately is vital for effective patient care. While experienced radiologists are proficient, they can still miss fractures, especially in complex cases. To address this, we propose a novel computer-aided detection system powered by cutting-edge machine learning and deep learning algorithms. Our approach is grounded in an extensive literature review, providing a solid foundation. Our primary objective is to create a highly effective image processing system capable of swiftly and accurately categorising bone fractures utilising X-ray and CT images. We use a MURA dataset contains about 20,000 x-ray images, including three different types of bones—elbow, hand, and shoulder and apply a range of processing approaches, such as pre-processing, segmentation, edge detection, and feature extraction. This allows us to categorize images as fractured or non-fractured, facilitating the comparison of different methods also classify the type of bone. This paper significance lies in its potential to aid medical professionals in promptly and accurately identifying bone fractures through advanced machine-learning techniques. With the increasing global incidence of fractures, our computer-aided diagnosis system could greatly enhance patient care and diagnostic accuracy. Furthermore, we plan to incorporate additional patient data, including medical history, seamlessly integrating it with X-ray images to improve the system's understanding of the patient's unique medical profile. This holistic approach holds the promise of revolutionizing fracture diagnosis and patient care.