HAND FRACTURE DETECTION USING DEEP LEARNING TECHNIQUES

Abstract

The effect of Artificial Intelligence Technology on healthcare is growing by the day. Numerous musculoskeletal disorders can be caused by common bone illnesses (MC). Worldwide, musculoskeletal conditions affect an estimated 1.71 billion people. A lot of work is being done to improve the efficiency of Bone fracture in the early stages, where medical imaging is crucial. In order to aid radiologists in the identification of bone fractures, deep learning (DL) and artificial intelligence (AI) are currently gaining a lot of interest. The application of DL in medical image analysis is widespread.. The most prevalent type of fracture has a large incidence rate, and that is the hand fracture. Hand fractures are typically detected by conventional radiography (i.e., X-ray imaging), however on occasion fracture delineation presents problems, necessitating a second confirmation by computed tomography (CT) for diagnosis. Recent developments in the Artificial Intelligence (AI) discipline of Deep Learning (DL) have demonstrated that Hand fracture identification may be automated using Convolutional Neural Networks. This study aims to detect a fracture on hand X-ray images using Deep learning to help orthopaedics in the diagnosis.