Paper Title
Radiographic Based Bone Fracture Detection Using Artificial Intelligence

Abstract
Bone fractures are a common injury and accurate detection is crucial for proper medical diagnosis and treatment. X-ray imaging is a widely used method, but the interpretation of images can be challenging even for experienced clinicians. Artificial Intelligence (AI) algorithms, such as Convolutional Neural Networks (CNNs) and Support Vector Machines (SVMs) have shown promise in aiding the detection of bone fractures from X-ray images. In this paper, we propose a bone fracture detection system that combines CNNs and SVMs for accurate and efficient detection. The system consists of two stages: feature extraction using a pre-trained CNN and classification using an SVM. The CNN is trained on a large dataset of images, enabling it to learn highlevel features to distinguish between different types of bone structures including fractures. The features are then input to the SVM for classification. The SVM is trained on a labelled dataset of X-ray images to classify the images based on the features extracted from the CNN. The proposed system achieves a high accuracy on dataset of X-ray images, demonstrating its effectiveness in detecting bone fractures. The proposed system has several advantages over traditional methods, including not relying on manual interpretation, handling high-dimensional data, and scalability for large datasets. The system's use of Artificial Intelligence automation allows for faster and more accurate diagnoses, making it a valuable tool in the medical field. Keyword - CNN, Support Vector Machine, Artificial Intelligence, Classification