The Project Report on Bone Fracture Detection Using CNN

Abstract

These days bone fracture is a common injury, and a precise diagnosis is critical for efficient treatment planning. Traditional fracture detection systems rely mainly on radiologists' manual scrutiny, which leads to subjective interpretations and probable diagnostic mistakes. The introduction of modern technologies, notably in the area of medical imaging process, has resulted in the development of computer-aided diagnosis systems that can aid in fracture detection. This research presents a thorough examination and comparison of X-ray-based bone fracture diagnosis systems. The research covers a wide range of algorithms, ML approaches, and deep learning methods used for fracture detection systems. The major goal is to assess these methods' performance and efficacy in terms of accuracy, sensitivity, specificity, and computing economy. This research seeks to shed light on the advantages, disadvantages, and future possibilities of X-ray-based bone fracture detecting systems. The findings will be useful to radiologists, medical practitioners, and researchers involved in medical image analysis. Finally, this paper serves as a thorough guide to X-ray bone fracture diagnosis, providing a critical examination of existing approaches as well as indicating areas for future research and development. It provides light on the potential of new imaging techniques and AI-based approaches to revolutionize fracture diagnosis, leading to more accurate diagnoses and better patient care in the long run.