Abstract:
This project focuses on improving the accuracy and efficiency of bone fracture detection in X-ray images using deep learning techniques. Traditional diagnostic methods often struggle with subtle or complex fractures, especially when used by less experienced medical practitioners, leading to delayed or incorrect assessments. To address these challenges, the proposed system employs two specialized deep learning models—a classification model and an object detection model—both built using Convolutional Neural Network (CNN) architectures.
The classification model is responsible for identifying whether a fracture is present in an X-ray image, while the object detection model localizes the fracture by generating bounding boxes around the affected region. These predicted bounding boxes are then used to estimate the size of the fracture, which further enables the system to provide an estimated healing time, offering additional support to medical professionals. Various model optimization techniques, including batch normalization and dropout, were applied to enhance performance and mitigate issues such as overfitting.
The system demonstrates strong results, with the classification model achieving an accuracy of 97%, indicating high reliability in distinguishing fractured from non-fractured images. The object detection model produced a mean Average Precision (mAP) of 73% and an average Intersection over Union (IoU) score of 60%, reflecting solid localization performance. These outcomes collectively suggest that the developed system is effective in automating fracture detection and has significant potential to support faster, more accurate diagnoses. By reducing diagnostic errors and assisting practitioners—particularly trainees—the system contributes to improved clinical decision-making in orthopedic care.
