ABSTRACT:

Bone fractures are common injuries that require prompt diagnosis and appropriate treatment to facilitate proper healing. Conventional methods for fracture detection, such as X-rays, often rely on subjective interpretation by radiologists and can be time-consuming. In recent years, deep learning techniques, particularly Convolutional Neural Networks (CNNs), have shown promising results in automating the detection of bone fractures from medical images. This study presents a novel approach to bone fracture detection using CNNs. The proposed model is trained on a large dataset of annotated X-ray images, consisting of both normal and fractured bone images. The CNN architecture leverages multiple convolutional layers to extract informative features from the input images and learns to differentiate between fractured and non-fractured bones.

INTRODUCTION:
Bone fractures are common injuries that can occur due to various factors, such as accidents, falls, sports-related activities, or underlying medical conditions. The accurate and timely detection of fractures is crucial for effective treatment planning and facilitating proper healing. Conventional methods for fracture detection, such as X-rays, rely on human expertise and can be subject to interpretation errors and time-consuming manual analysis. One of the most prominent techniques used for automated fracture detection is Convolutional Neural Networks(CNNs). CNNs are deep learning models that have demonstrated remarkable success in various computer vision tasks, including image classification and object detection. Their ability to learn hierarchical features directly from raw image data makes them well-suited for analyzing medical images, such as X-rays.

Fractures are one of the most common issues that each living body faces. Even doctors are overlooking minor fractures that might lead to more serious injury in the future. Through x-rays are there but it might be difficult to tell if it is broken or not. If you break a bone, you must treat it as a medical emergency and get care as soon as possible. Fractures come in a various shapes and sizes. When there is a break in the bone, it is called a fracture. Traumatic fractures are more prevalent and are caused by a rapid fall, high pressure, unnecessary fighting, accidents, or any other reason, whereas a pathological fracture is caused by a medical condition of the bone. So, this project is to find the best accurate Convolutional Neural Network model which is a step-bystep picture analyzing algorithm that aids us in providing better results to detect bone fractures.

Objective of the project

Convolutional Neural Networks (CNNs), have shown promising results in automating the detection of bone fractures from medical images. This study presents a novel approach to bone fracture detection using CNNs.