Detection of Hand Bone Fractures in X-Ray Images Using Hybrid YOLO NAS

Abstract

The majority of bones that have fractured in humans are hand bones. As we use our hands widely, they need early and accurate detection to be diagnosed. Fractures in the hands are most frequently brought on by blunt force trauma, sports injuries, and bone fragility. Getting an X-ray of the affected area of the bone and then discussing the results with a medical practitioner or radiologist is the standard procedure for determining whether or not a fracture exists in the bone. The majority of medical professionals and radiologists use X-rays to diagnose hand fractures; however, in some instances, they might miss small or hairline fractures. Additionally, it might be difficult to find a good radiologist who can detect the fracture properly and in time, because a delay in diagnosis can cause the injury to be more severe, and the bone might not be recovered properly. Therefore, to detect hand bone and joint fractures through X-rays, a hybrid model was developed that uses deep learning algorithms YOLO NAS (You Only Look Once - Neural Architecture Search), Efficient Det, and DETR3 (DEtection TRansformer), which are widely recognized for their exact object detection capabilities. The dataset used for this model is a hybrid dataset of 4736 hand-bone X-ray images, they were further classified into 6 classes based on their types. To evaluate the performance the best method is to compare the proposed model with the existing models, hence, the model was compared with various existing algorithms and result analysis was done.

Keywords

• Hand bone fracture
• fracture detection
• X-rays
• YOLO NAS
• deep learning
• hybrid dataset