BONE FRACTURE DETECTION USING CONVOLUTIONAL NEURAL NETWORK (CNN)

Abstract

Our project focuses on the development of an automated bone fracture detection system using Convolutional Neural Networks (CNNs). Manual interpretation of X-ray images for fracture detection can be time-consuming and subjective. To address this, we propose a deep learning-based solution capable of accurately identifying fractures with high efficiency. The project involves training a CNN architecture on a diverse dataset of annotated X-ray images containing various types of fractures. We will employ data augmentation techniques to enhance model generalization and transfer learning with pre-trained models like ResNet or VGG to expedite training and improve performance. Evaluation of the system will be conducted on multiple datasets from medical institutions, assessing metrics such as accuracy, sensitivity, specificity, and F1 score. Integration of the developed system into a user-friendly interface will enable healthcare professionals to upload X-ray images and receive automated fracture detection results in real-time, streamlining the diagnostic process and improving patient care. Overall, our project aims to revolutionize bone fracture detection by leveraging deep learning techniques, enhancing diagnostic accuracy, and reducing the workload of radiologists.