An efficient deep learning approach to detect bone fractures from X-ray images

Abstract

We recommend an effective,reliable and efficient deep-learning approach to classify bone fractures using X-ray images. The proposed system comprises several steps as dataset collection, preprocessing, and categorization of fractures. The dataset contains different types of X-ray images for various fractures and annotated as fractured and non-fractured cases. Dataset was collected from Kaggle. The images were processed through resizing,augmentation to be standardized for deep learning models. The classification of fractures utilizes neural network paged deep learning models as the last process. The research uses Inception,VGG-19 and a custom CNN model for fracture detection while using Grad-CAM to demonstrate how each model makes its decisions. These models utilize CNN architectures to overcome current models accuracy limitations when detecting fractures. The study also shows how CNN-based approaches elevate the precision of bone fracture detection which creates potential benefits for medical use and improved patient healthcare.