Fracture_Detection_Improved_YOLOv8 and YOLOv9-Fracture-Detection
These are sequential versions of the same research line where YOLOv9-based detection represents a newer model iteration compared to the YOLOv8-AM predecessor, making them competitors for the same pediatric wrist fracture detection task rather than complementary tools.
About Fracture_Detection_Improved_YOLOv8
RuiyangJu/Fracture_Detection_Improved_YOLOv8
[ICONIP 2024] [IEEE Access 2025] YOLOv8-AM: YOLOv8 with Attention Mechanisms for Pediatric Wrist Fracture Detection
This project helps medical professionals, specifically radiologists and orthopedic specialists, automatically detect pediatric wrist fractures in X-ray images. By inputting raw X-ray images, the system identifies and highlights potential fracture locations, helping to reduce diagnostic errors and speed up the interpretation process. It's designed for clinical settings where rapid and accurate fracture detection is critical for young patients.
About YOLOv9-Fracture-Detection
RuiyangJu/YOLOv9-Fracture-Detection
[Electronics Letters 2024] YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images
This project helps medical professionals, specifically radiologists and emergency room physicians, detect fractures in pediatric wrist X-ray images. It takes an X-ray image as input and identifies potential fracture locations, outputting a marked image that highlights these areas. This tool assists in quickly and accurately pinpointing fractures in young patients' wrists.
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