Segmentation and Classification of Bone Fractures in X-Ray Images using Deep Learning

It is vital for human life to expedite the detection of bone fractures by examining the X-ray images in a period of time and providing for medical diagnostics that impact the final treatment and thus the recovery of the patient. In the traditional way of fracture diagnosis, the results rely on the knowledge of the radiologist. The doctors often make errors, have delays in making decisions, or inconsistencies in the diagnostics, especially, and it is the case with the fractures that are a little difficult to detect. The demand for automation in the radiology and the need for efficient diagnostic tools are the major issues that bring to the force the significance of the application of deep learning techniques. In this proposal, an innovative technique based on the custom Convolutional Neural Network (CNN) is suggested. This framework hits at the efficient extraction of features from the X-ray images of bone fractures and their classification by using a special CNN model for the task. The proposed system adopts a two-stage architecture. First, the custom CNN is employed for precise segmentation of X-ray images, isolating relevant anatomical structures and fracture regions. In the second stage, the same custom CNN architecture, further fine-tuned, is used to classify the extracted fractures by type and severity. Unlike generic CNN models, this custom-designed approach ensures optimized performance by tailoring the architecture to the specific challenges of bone fracture detection, including handling variability in image quality and subtle fracture patterns. This two-stage process helps model to focus on ROI within X-ray images, significantly enhancing accuracy and precision. This work highlights how a custom CNN can revolutionize medical imaging by providing a tailored solution for segmentation and classification of fractures. The emphasis on a custom architecture ensures that the system is not only accurate and reliable but also well-suited for clinical integration. This innovation has the potential to simplify workflows, improve accuracy, enhance patient care, particularly in time-sensitive environments like emergency departments.