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.
