Bone_Fracture_Detection_YOLOv8

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Bone_Fracture_Detection_YOLOv8

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[Scientific Reports 2023] Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm

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# YOLOv8 for Fracture Detection

>[Fracture Detection in Pediatric Wrist Trauma X-ray Images Using YOLOv8 Algorithm](https://arxiv.org/abs/2304.05071)

## :tada::tada::tada: NEWS: [Our split dataset is available now !!!](https://ruiyangju.github.io/GRAZPEDWRI-DX_JU/)

### YOLOv8 architecture

 <img src="img/figure_details.jpg" width="640" title="details">

## Citation
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}
    }

## Environment
```
  pip install -r requirements.txt
```

## Dataset
### Dataset Split
* You can find the original GRAZPEDWRI-DX dataset [here](https://figshare.com/articles/dataset/GRAZPEDWRI-DX/14825193) (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_id` stored in `dataset.csv`.
  You can download our split dataset [here](https://1drv.ms/u/s!Ap6uuRvdVcJWbXtfIFYUvzOMKXQ).
* 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
* 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
                         └── ...
                         
## Model
You can get the open source code of YOLOv8 through [YOLOv8 official GitHub](https://github.com/ultralytics/ultralytics).
### Train
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

* 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
```

### Trained Model
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
```

### Validate 
* Arguments

* CLI
```
  yolo val model=/path/to/best.pt data=/path/to/meta.yaml
```

### Predict
* CLI
```
  yolo predict model=/path/to/best.pt source=/path/to/image
```

* Example
```
  yolo predict model=best.pt source='./test.png'
```

## Experimental Results

<img src="img/figure_result.jpg" width="1024" title="result">

The prediction examples of our model on the pediatric wrist trauma X-ray images. (a) the manually labeled images, (b) the predicted images.

## Application
For research project agreement, we don't release APP code, please refer to [YOLOv7 Bone Fracture Detection](https://github.com/mdciri/YOLOv7-Bone-Fracture-Detection) and our paper for details.

### Fracture Detection Using YOLOv8 App

 <img src="img/figure_application.jpg" width="1024" title="application">

## Related Works

<details><summary> Expand </summary>

* [https://github.com/RuiyangJu/Fracture_Detection_Improved_YOLOv8](https://github.com/RuiyangJu/Fracture_Detection_Improved_YOLOv8)
* [https://github.com/RuiyangJu/YOLOv9-Fracture-Detection](https://github.com/RuiyangJu/YOLOv9-Fracture-Detection)
* [https://github.com/RuiyangJu/YOLOv8_Global_Context_Fracture_Detection](https://github.com/RuiyangJu/YOLOv8_Global_Context_Fracture_Detection)
* [https://github.com/RuiyangJu/FCE-YOLOv8](https://github.com/RuiyangJu/FCE-YOLOv8)

</details>

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