YOLOv8-AM: YOLOv8 with Attention Mechanisms for Pediatric Wrist Fracture Detection

Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm

YOLOv9 for Fracture Detection in Pediatric Wrist Trauma X-ray Images

Detection of cracks in the building foundation

Indicates the location of wrist fractures in x-rays through training with yolo v8 of roboflow images downloaded from https://www.kaggle.com/datasets/pkdarabi/bone-fracture-detection-computer-vision-project/code

From a selection of data from the Roboflow file https://universe.roboflow.com/landy-aw2jb/fracture-ov5p1/dataset/1, which represents a reduced but homogeneous version of that file, a model is obtained based on yolov10 with that custom dataset to indicate fractures in x-rays.

Global Context Modeling in YOLOv8 for Pediatric Wrist Fracture Detection

This project uses PyTorch to classify bone fractures. As well as fine-tuning some famous CNN architectures (like VGG 19, MobileNetV3, RegNet,...), we designed our own architecture. Additionally, we used Transformer architectures (such as Vision Transformer and Swin Transformer). This dataset is Bone Fracture Multi-Region X-ray, available on Kaggle.

From dataset https://universe.roboflow.com/roboflow-100/bone-fracture-7fylg a model is obtained, based on yolov10, with that custom dataset, to indicate fractures in x-rays.

From a selection of data from the Roboflow file https://universe.roboflow.com/landy-aw2jb/fracture-ov5p1/dataset/1, which represents a reduced but homogeneous version of that file, a model is obtained using an adaptation of the project https://www.kaggle.com/code/nyachhyonjinu/yolov3-test instead any yolo model