EDGE AI BASED BONE FRACTURE DETECTION USING TFLITE

Bone fractures are common injuries that require prompt diagnosis and treatment to ensure optimal patient outcomes. However, accurately identifying fractures in medical imaging such as X-rays can be challenging, particularly in environments with limited access to specialized medical expertise. In this paper, we propose an Edge AI-based Bone Fracture Detection system using TensorFlow Lite (TFLite) for deployment on edge devices. Our system leverages deep learning models trained on annotated X-ray images to automatically detect and localize bone fractures in real-time. By deploying the detection model on edge devices, such as smartphones or portable X-ray machines, we enable rapid and decentralized fracture diagnosis, facilitating timely medical interventions. We evaluate the performance of our system using a dataset of labeled X-ray images and demonstrate its effectiveness in accurately detecting bone fractures with high precision and recall. The proposed Edge AI-based Bone Fracture Detection system offers a cost-effective and accessible solution for improving fracture diagnosis in resource-constrained healthcare settings, ultimately enhancing patient care and outcomes.