FCE-YOLOv8

Pediatric Wrist Fracture Detection Using Feature Context Excitation Modules in X-ray Images

Comparison

Performance

Model	Test Size	Method	Param.	FLOPs	F1 Score	AP50val	AP50-95val	Speed
YOLOv8	1024	-	43.61M	164.9G	0.62	63.58%	40.40%	7.7ms
YOLOv8+SA	1024	-	43.64M	165.4G	0.63	64.25%	41.64%	8.0ms
YOLOv8+ECA	1024	-	43.64M	165.5G	0.65	64.24%	41.94%	7.7ms
YOLOv8+GAM	1024	-	49.29M	183.5G	0.65	64.26%	41.00%	12.7ms
YOLOv8+ResGAM	1024	-	49.29M	183.5G	0.64	64.98%	41.75%	18.1ms
YOLOv8+ResCBAM	1024	-	53.87M	196.2G	0.64	65.78%	42.16%	8.7ms
YOLOv9	1024	-	69.42M	244.9G	0.66	65.62%	43.73%	16.1ms
YOLOv8+GC	1024	3	43.85M	165.6G	0.66	66.32%	42.85%	7.9ms
YOLOv8+GCT	1024	2	43.64M	165.4G	0.64	65.67%	41.98%	15.3ms
YOLOv8+SE	1024	3	43.85M	165.6G	0.66	67.07%	41.85%	15.3ms
YOLOv8+GE	1024	2	43.65M	165.4G	0.64	65.99%	42.02%	15.3ms

Citation

If you find our paper useful in your research, please consider citing:

  @article{ju2024fce,
    title={FCE-YOLOv8: YOLOv8 with Feature Context Excitation Modules for Fracture Detection in Pediatric Wrist X-ray Images},
    author={Ju, Rui-Yang and Chien, Chun-Tse and Xieerke, Enkaer and Chiang, Jen-Shiun},
    journal={arXiv preprint arXiv:2410.01031},
    year={2024}
  }

Requirements

• Linux (Ubuntu)
• Python = 3.9
• Pytorch = 1.13.1
• NVIDIA GPU + CUDA CuDNN

Environment

  pip install -r requirements.txt

Dataset Download

• You can download the GRAZPEDWRI-DX Dataset on this Link.

Dataset Split

• To split the dataset into training set, validation set, and test set, you should first put the image and annotatation into ./GRAZPEDWRI-DX/data/images, and ./GRAZPEDWRI-DX/data/labels.

• And then you can split the dataset as the following step:

    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. The script then will move the files into the relative folder as it is represented here below.

   GRAZPEDWRI-DX
      └── 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
      └── 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
                     └── ...
  

Experimental setup

• We have provided a training set, test set and validation set containing a single image that you can run directly by following the steps in the example below.
• Before training the model, make sure the path to the data in the ./GRAZPEDWRI-DX/data/meta.yaml file is correct.

  # patch: /path/to/GRAZPEDWRI-DX/data
  path: 'E:/GRAZPEDWRI-DX/data'
  train: 'images/train_aug'
  val: 'images/valid'
  test: 'images/test'

Train

Arguments

You can set the value in the ./ultralytics/cfg/default.yaml.

Key	Value	Description
model	None	path to model file, i.e. yolov8m.yaml, yolov8m_GC_M1.yaml
data	None	path to data file, i.e. coco128.yaml, meta.yaml
epochs	100	number of epochs to train for, i.e. 100, 150
patience	50	epochs to wait for no observable improvement for early stopping of training
batch	16	number of images per batch (-1 for AutoBatch), i.e. 16, 32, 64
imgsz	640	size of input images as integer, i.e. 640, 1024
save	True	save train checkpoints and predict results
device	0	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

• Training Steps:

  python start_train.py -model path to model file --data_dir  path to data file

Method 1

• Example (YOLOv8+GC-M, YOLOv8-GCT-M, YOLOv8-SE-M, YOLOv8-GE-M):

  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_GC_M1.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml
  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_GCT_M1.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml
  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_SE_M1.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml
  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_GE_M1.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml

Method 2

• Example (YOLOv8+GC-M, YOLOv8-GCT-M, YOLOv8-SE-M, YOLOv8-GE-M):

  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_GC_M2.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml
  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_GCT_M2.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml
  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_SE_M2.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml
  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_GE_M2.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml

Method 3

• Example (YOLOv8+GC-M, YOLOv8-GCT-M, YOLOv8-SE-M, YOLOv8-GE-M):

  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_GC_M3.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml
  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_GCT_M3.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml
  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_SE_M3.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml
  python start_train.py --model ./ultralytics/cfg/models/v8/yolov8m_GE_M3.yaml --data_dir ./GRAZPEDWRI-DX/data/meta.yaml

Related Works

Expand

• https://github.com/RuiyangJu/Bone_Fracture_Detection_YOLOv8
• https://github.com/RuiyangJu/Fracture_Detection_Improved_YOLOv8
• https://github.com/RuiyangJu/YOLOv9-Fracture-Detection
• https://github.com/RuiyangJu/YOLOv8_Global_Context_Fracture_Detection