Automated Bone Fracture Detection Using Convolutional Neural Network

Abstract

Abstract Bones are an important part of the human body. Humans are prone to bone fractures, which can occur as a result of tremendous pressure being applied to the bone, or as a result of a simple accident. As a result, in the medical field, an accurate diagnosis of a bone fracture is crucial. In this work, bone fractures are examined using X-ray/CT scans. The purpose of this research is to develop an image processing based system that can classify bone fractures rapidly and accurately using data from x-ray and CT images. In many diagnostic and therapeutic applications, automatic fault detection in MRI and CT images is critical. Tumor segmentation and classification are difficult due to the large amount of data in MR images and the fuzzy boundaries. This project proposed an automatic bone fracture detection system that improves accuracy and yield while cutting down on diagnosis time. The aim is to classify into two categories: normal, fractured. The amount of data in MR and CT pictures is too great for human interpretation and analysis. Bone fracture segmentation in Magnetic Resonance Imaging (MRI) has appeared as an emerging subject study in the realm of medical imaging systems in recent years. The potential to accurately recognise the size and location of a bone fracture is critical in the diagnosis of a fracture. Pre-processing of MR images, feature extraction, and classification are the four stages of the diagnosis procedure. The features are extracted using wavelet transformation after the image has been preprocessed (DWT). Automated fracture identification is essential in a computer-aided telemedicine system. Human arbitrary bones frequently fracture as a consequence of accidental traumas like slipping. Computer-aided diagnosis (CAD) relieves doctors’ workload while also detecting fractures. A new classification network sensitive to fracture lines is described, called Crack-Sensitive Convolutional Neural Network (CrackNet). Faster Region with Convolutional Neural Network (Faster R-CNN) is used to identify 20 various types of bone areas in X-ray pictures, and CrackNet is used to assess incase each bone area is broken. The classification accuracy for 100 training and test sets is 99.5%.