Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm
🎉🎉🎉 NEWS: Our split dataset is available now !!!
If you find our paper useful in your research, please consider citing:
@article{ju2023fracture,
title={Fracture detection in pediatric wrist trauma X-ray images using YOLOv8 algorithm},
author={Ju, Rui-Yang and Cai, Weiming},
journal={Scientific Reports},
volume={13},
number={1},
pages={20077},
year={2023},
publisher={Nature Publishing Group UK London}
}
pip install -r requirements.txt
-
You can find the original GRAZPEDWRI-DX dataset here (unsplit).
-
Download dataset and put images and annotatation into
./GRAZPEDWRI-DX_dataset/data/images,./GRAZPEDWRI-DX_dataset/data/labels.python split.py -
The dataset is divided into training, validation, and testing set (70-20-10 %) according to the key
patient_idstored indataset.csv. You can download our split dataset here. -
The script then will move the files into the relative folder as it is represented here below.
GRAZPEDWRI-DX_dataset └── data ├── meta.yaml ├── images │ ├── train │ │ ├── train_img1.png │ │ └── ... │ ├── valid │ │ ├── valid_img1.png │ │ └── ... │ └── test │ ├── test_img1.png │ └── ... └── labels ├── train │ ├── train_annotation1.txt │ └── ... ├── valid │ ├── valid_annotation1.txt │ └── ... └── test ├── test_annotation1.txt └── ...
The script will create 3 files: train_data.csv, valid_data.csv, and test_data.csv with the same structure of dataset.csv.
- Data augmentation of the training set using the addWeighted function doubles the size of the training set.
python imgaug.py --input_img /path/to/input/train/ --output_img /path/to/output/train/ --input_label /path/to/input/labels/ --output_label /path/to/output/labels/
For example:
python imgaug.py --input_img ./GRAZPEDWRI-DX/data/images/train/ --output_img ./GRAZPEDWRI-DX/data/images/train_aug/ --input_label ./GRAZPEDWRI-DX/data/labels/train/ --output_label ./GRAZPEDWRI-DX/data/labels/train_aug/
-
The path of the processed file is shown below:
GRAZPEDWRI-DX_dataset └── data ├── meta.yaml ├── images │ ├── train │ │ ├── train_img1.png │ │ └── ... │ ├── train_aug │ │ ├── train_aug_img1.png │ │ └── ... │ ├── valid │ │ ├── valid_img1.png │ │ └── ... │ └── test │ ├── test_img1.png │ └── ... └── labels ├── train │ ├── train_annotation1.txt │ └── ... ├── train_aug │ ├── train_aug_annotation1.txt │ └── ... ├── valid │ ├── valid_annotation1.txt │ └── ... └── test ├── test_annotation1.txt └── ...
You can get the open source code of YOLOv8 through YOLOv8 official GitHub.
Before training the model, make sure the path to the data in the meta.yaml file is correct.
- meta.yaml
# patch: /path/to/GRAZPEDWRI-DX/data
path: 'E:/GRAZPEDWRI-DX/data'
train: 'images/train_aug'
val: 'images/valid'
test: 'images/test'
- Arguments
| Key | Value | Description |
|---|---|---|
| model | None | path to model file, i.e. yolov8n.pt, yolov8n.yaml |
| data | None | path to data file, i.e. coco128.yaml |
| epochs | 100 | number of epochs to train for |
| patience | 50 | epochs to wait for no observable improvement for early stopping of training |
| batch | 16 | number of images per batch (-1 for AutoBatch) |
| imgsz | 640 | size of input images as integer, i.e. 640, 1024 |
| save | True | save train checkpoints and predict results |
| device | None | device to run on, i.e. cuda device=0 or device=0,1,2,3 or device=cpu |
| workers | 8 | number of worker threads for data loading (per RANK if DDP) |
| pretrained | True | (bool or str) whether to use a pretrained model (bool) or a model to load weights from (str) |
| optimizer | 'auto' | optimizer to use, choices=SGD, Adam, Adamax, AdamW, NAdam, RAdam, RMSProp, auto |
| resume | False | resume training from last checkpoint |
| lr0 | 0.01 | initial learning rate (i.e. SGD=1E-2, Adam=1E-3) |
| momentum | 0.937 | SGD momentum/Adam beta1 |
| weight_decay | 0.0005 | optimizer weight decay 5e-4 |
| val | True | validate/test during training |
- Example
cd Bone_Fracture_Detection_YOLOv8
yolo train model=yolov8n.pt data=./GRAZPEDWRI-DX/data/meta.yaml epochs=100 batch=16 imgsz=640 save=True device=0 workers=4 pretrained=yolov8n.pt optimizer=SGD lr0=0.01
Use gdown to download the trained model from our GitHub:
gdown https://github.com/RuiyangJu/Bone_Fracture_Detection_YOLOv8/releases/download/Trained_model/best.pt
- Arguments
| Key | Value | Description |
|---|---|---|
| data | None | path to data file, i.e. coco128.yaml |
| imgsz | 640 | size of input images as integer, i.e. 640, 1024 |
| batch | 16 | number of images per batch (-1 for AutoBatch) |
| save_json | False | save results to JSON file |
| save_hybrid | False | save hybrid version of labels (labels + additional predictions) |
| conf | 0.001 | object confidence threshold for detection |
| iou | 0.6 | intersection over union (IoU) threshold for NMS |
- CLI
yolo val model=/path/to/best.pt data=/path/to/meta.yaml
- CLI
yolo predict model=/path/to/best.pt source=/path/to/image
- Example
yolo predict model=best.pt source='./test.png'
For research project agreement, we don't release APP code, please refer to YOLOv7 Bone Fracture Detection and our paper for details.
