The idea of bone fracture detection and classification in X-ray images utilizing machine learning is outlined in this research. The X-ray images are fed into a neural network model that has been trained on a sizable coaching dataset corresponding to various sorts of fractures. To improve the categorization and identification of bone fractures using deep learning, coaching data and input settings have been fine-tuned. Python is used to create a software system that can import the image to be identified and supply the model with insights about the fracture. The project utilizes the same image dataset and contrasts CNN and MobileNet models, and also incorporates a hybrid model combining MobileNet with Random Forest.

Keywords: Bone fracture, Deep Learning, Fracture classification, CNN, MobileNet, Hybrid model.