Deep learning based advance image processing for bone fracture detection

Abstract

Bone fractures are widespread in different age groups; hence, making the medical interventions more effective and accurate demands the application of the most precise diagnostic techniques. The traditional procedures for the detection of fractures typically involve many errors and are labor-intensive because a doctor has to do interpretation of the radiography scans. This study proposes the use of deep learning-enhanced image processing system as an automated system to overcome the aforementioned difficulties in bone fracture detection. The system enhances and boosts the fracture diagnosis accuracy by using the latest methods, such as convolutional neural networks (CNNs). A systematic way begins with a number of annotated datasets of medical images that ranges from X-rays, CT scans to MRIs. CNNs are trained to identify features of fractures, whereas preprocessing steps aim at size normalization and image quality improvement. Hyperparameter tuning enhances the model's performance through the dataset division into the training, validation, and testing parts. The measuring standards like precision and accuracy justify the validity of the model. Healthcare professionals can be absolutely certain about practical implementation by an easy integration in current healthcare systems and also with a creation of an intuitive interface. The project takes into account the scalability, the ethical issues, and the regulatory compliance. The goal is to create such a system that exponentially boosts the accuracy, speed, and efficiency of fracture recognition in the medical field, where the damage of bones is prominently diagnosed.