BONE FRACTURE DETECTION USING DEEP LEARNING

The project focuses on developing an accurate and efficient system for fracture detection leveraging the diverse MURA X-ray images dataset. Utilizing a Convolutional Neural Network (CNN) and transfer learning with a pre-trained ResNet50 model, the project aims to automate the detection of bone fractures. The dataset undergoes preprocessing, and the model. is trained with data augmentation techniques such as horizontal flipping to enhance generalization. The evaluation phase assesses the model’s accuracy, precision, and recall on a separate test set, highlighting its potential as a valuable tool for healthcare professionals. By automating fracture detection, the project contributes to expedited medical diagnostics, showcasing the transformative impact of machine learning in medical image analysis.