Splitting data distribution
包含手机拍摄的六类皮肤病变图像及患者年龄性别，用于皮肤癌分类模型训练与评估。
基本信息
资源简介
Splitting data distribution
【数据集背景】
本数据集源站标题为《Splitting data distribution.》。源站首次发布：2025-07-28。
【数据内容】
源站原始描述：Skin cancer is a significant global public health issue, with millions of new cases identified each year. Recent breakthroughs in artificial intelligence, especially deep learning, possess considerable potential to enhance the accuracy and efficiency of screening. This study proposes an approach that employs smartphone images, which are preprocessed using adaptive learning and Black-Hat transformation. ViT is utilized for feature extraction, and a stacking model is constructed employing these fe…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 3.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0328402.t003。
【主题与分类】
源站学科分类：Space Science、Medicine、Immunology、Biological Sciences not elsewhere classified、Cancer、Science Policy。主题标签：squamous cell carcinoma、possess considerable potential、new cases identified、especially deep learning、enhancing diagnostic sensitivity、basal cell carcinoma、suggested model attained、model &# 8217、like patient age、associated skin diseases。
【适用方向】
影像类数据可用于医学图像分析模型（检测、分割、分类）的训练与评测，也可用于标注质量与成像参数相关性的研究。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Splitting_data_distribution_/29657926
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Mertahi, Adil El; Ezzine, Hind; Douzi, Samira; Douzi, Khadija (2025). Splitting data distribution.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Splitting data distribution.
- DOI：10.1371/journal.pone.0328402.t003
- 发布日期：2025-07-28
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Splitting_data_distribution_/29657926
命令行下载
curl -L -o "Table 3.xls" "https://ndownloader.figshare.com/files/56611210"
文件清单
Table 3.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Skin cancer is a significant global public health issue, with millions of new cases identified each year. Recent breakthroughs in artificial intelligence, especially deep learning, possess considerable potential to enhance the accuracy and efficiency of screening. This study proposes an approach that employs smartphone images, which are preprocessed using adaptive learning and Black-Hat transformation. ViT is utilized for feature extraction, and a stacking model is constructed employing these features in conjunction with image-related variables, like patient age and sex, for final classification. The model’s efficacy in identifying cancer-associated skin diseases was evaluated across six categories of skin lesions: actinic keratosis, basal cell carcinoma, melanoma, nevus, squamous cell carcinoma, and seborrheic keratosis. The suggested model attained an overall accuracy of 97.61%, with a PVV of 96.88%, a recall of 97.63%, and an F1 score of 97.19%, so illustrating its efficacy in detecting malignant skin lesions. This method could greatly aid dermatologists by enhancing diagnostic sensitivity and specificity, reducing delays in identifying the most suspicious lesions, and ultimately reaching more patients in need of timely screenings and patient care, thus saving lives.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0328402.t003（source: https://plos.figshare.com/articles/dataset/Splitting_data_distribution_/29657926）。
数据缺失？
依托陕西、四川两大基地，我们与超过十家三甲医院建立直接合作关系，覆盖合规授权、采集治理、专业标注、数据交付的全流程，为AI医疗团队提供即拿即用的高质量临床数据。
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