AI-Powered Bone Fracture Detection System with Vision Transformer and CNN Deep Learning Models for Medical Imaging

Abstract

The purpose of the study was to identify the AI-Powered Bone Fracture Detection System, an automated bone fracture recognition device, which, as well as allowing detecting and categorizing bone fractures with the help of deep learning models, can provide a device with practical use in the hospitals. It is a system in which a Convolutional Neural Network (CNN) is incorporated to identify fractures, a vision transformer (ViT) model is incorporated to identify fractures, and a YuLOnly-based system to identify fractures. CNN model is used to identify the presence of a fracture in some of the images and the Vision Transformer model is used to classify the fracture based on the different types, e.g., avulsion, comminuted, and spiral fractures. The Tagging of the regions that the fractures have taken place in the images is done using the YOLO model to identify the fracture. Also, heatmaps are generated using GradCAM, to give a reasonable understanding of how the model arrives at a decision. The system is integrated to work with clinical workflow and capable of making more timely and accurate diagnosis of fractures hence help in faster decisions in treatment. The combination of these models makes the accuracy of their results very high in identifying and categorizing the results as effective medical analysis and enhanced patient outcome.

Ähnliche Arbeiten