Deep Learning-Based Fracture Localization in X-Ray Images Using Locally Collected Medical Data.

Abstract

Bone fractures are among the most common musculoskeletal injuries and require accurate and timely diagnosis for effective patient management. Recently, deep learning techniques have aided diagnosis systems for medical image analysis. -n strong potential in computershowHowever, developing reliable fracture detection models remains challenging due to the limited annotated and clinically representative datasets. -availability of wellbased framework for automated fracture analysis using X-udy proposes a deep learningThis stray images. A locally collected dataset from a regional hospital was curated and organized to level diagnostic -esupport both classification and localization tasks. The dataset includes imaglabels and detailed fracture localization annotations, along with relevant clinical information to enhance its practical medical value. stage pipeline. For fracture classification, transfer -The proposed approach follows a multiwith DenseNet architectures was explored, including intermediate pretraining on the learning MURA dataset to improve domain adaptation. For fracture localization and segmentation, a YOLOv11 Nano Segmentation model was employed. related medical pretraining improves -raging domainExperimental results show that leveclassification performance and feature representation. In addition, the YOLOv11 Nano model ray images with satisfactory accuracy. Overall, the -successfully localizes fracture regions in Xate that combining locally collected clinical data, structured annotations, and findings demonstrmodern deep learning techniques can significantly enhance automated fracture analysis and aided diagnosis systems.تُعدّ كسور العظام من أكثر إصابات الجهاز العضلي الهيكلي شيوعًا، وتتطلب تشخيصًا دقيقًا وفي الوقت المناسب من أجل إدارة فعالة للمرضى. في الآونة الأخيرة، ساعدت تقنيات التعلم العميق أنظمة التشخيص في تحليل الصور الطبية.