烧伤程度标签分布数据集

数据集获取（Figshare）

• 源站标题：<p>Label distribution according to burn degree.</p>
• DOI：10.1371/journal.pone.0344042.t001
• 发布日期：2026-03-18
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/_p_Label_distribution_according_to_burn_degree_p_/31805302

命令行下载

curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/62928466"

文件清单

• Table 1.xls（5.63 KB）

文件总体积 5.63 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

Accurate classification of skin burn depth is vital for determining appropriate treatment and accelerating the healing process. This study conducts a comparative analysis of YOLO-based deep learning architectures for the automated classification of skin burns. Analyses were performed on a robust, multi-source dataset created by combining a proprietary collection of 358 retrospective images from Karadeniz Technical University Farabi Hospital with two large public datasets from Roboflow Universe and Kaggle. All images were meticulously labeled into four burn degrees by expert general surgeons. To enhance model performance and generalizability, various data augmentation and preprocessing techniques were applied. Segmentation-based versions of YOLOv8 and YOLOv11 with different architectural sizes (medium, large, extra-large) were evaluated using metrics such as precision, recall, F1-Score, and mAP. The findings revealed that the YOLOv11x-seg model demonstrated marked superiority over all other tested architectures, achieving an F1-Score of 0.87 and a mAP@0.5 of 0.91. Statistical analysis confirmed the significance of these results. The study demonstrates that the YOLOv11x-seg architecture offers significant potential as a rapid and objective decision support tool in clinical settings. This work makes an original contribution to improving burn diagnosis by integrating a state-of-the-art deep learning model into medical image analysis.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0344042.t001（source: https://plos.figshare.com/articles/dataset/_p_Label_distribution_according_to_burn_degree_p_/31805302）。