工作中使用的核心库版本
该数据集提供了皮肤癌分类研究中深度学习、联邦学习及可解释AI框架所依赖的核心库版本清单。
基本信息
资源简介
工作中使用的核心库版本
【数据集背景】
本数据集源站标题为《Versions of the core libraries used in this work.》。源站首次发布：2025-07-16。
【数据内容】
源站原始描述：Skin cancer (SC) is the most prominent form of cancer in humans, with over 1 million new cases reported worldwide each year. Early identification of SC plays a crucial role in effective treatment. However, protecting patient data privacy is a major concern in medical research. Therefore, this study presents a smart framework for classifying SC leveraging deep learning (DL), federated learning (FL) and explainable AI (XAI). We tested the presented framework on two well-known datasets, ISBI2016 an…
【数据结构与技术规格】
文件数 1 个，合计 5.63 KB。Table 5.xls：application/vnd.ms-excel，5.63 KB。数据集 DOI：10.1371/journal.pone.0324393.t005。
【主题与分类】
源站学科分类：Space Science、Genetics、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Science Policy。主题标签：inceptionresnetv2 &# 8212、ensure model interpretability、vgg16 algorithm showed、maintain data privacy、densenet169 algorithm obtained、proposed framework offers、proposed framework、smart framework、presented framework、two well。
【适用方向】
文本类数据可用于医学自然语言处理，如命名实体识别、信息抽取与文本分类。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Versions_of_the_core_libraries_used_in_this_work_/29584906
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Biswas, Shuvo; Saha, Sajeeb; Uddin, Muhammad Shahin; Mostafiz, Rafid (2025). Versions of the core libraries used in this work.. PLOS ONE. Dataset
下载信息
注册下载
需要注册 Kaggle 账号并登录后下载，适合需要跟踪下载记录和使用 API 的用户。
暂未开放公开下载
无需注册即可直接获取公开样本或文档，适合快速预览和评估数据集质量。
免登录有偿下载
公开数据集受托下载与技术交付服务。
提供高速下载与技术交付服务（收技术服务费，非数据销售）
暂未开放当前数据集主要来源为 Kaggle 公开托管，完整影像包建议通过原始链接或 Kaggle API 获取。
使用方式
数据集获取（Figshare）
- 源站标题：Versions of the core libraries used in this work.
- DOI：10.1371/journal.pone.0324393.t005
- 发布日期：2025-07-16
- 文件数：1 个
- 文件总体积：5.63 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Versions_of_the_core_libraries_used_in_this_work_/29584906
命令行下载
curl -L -o "Table 5.xls" "https://ndownloader.figshare.com/files/56328217"
文件清单
Table 5.xls（5.63 KB）
文件总体积 5.63 KB（源站 API 实测）。
数据说明
Skin cancer (SC) is the most prominent form of cancer in humans, with over 1 million new cases reported worldwide each year. Early identification of SC plays a crucial role in effective treatment. However, protecting patient data privacy is a major concern in medical research. Therefore, this study presents a smart framework for classifying SC leveraging deep learning (DL), federated learning (FL) and explainable AI (XAI). We tested the presented framework on two well-known datasets, ISBI2016 and ISBI2017. The data was first preprocessed by several techniques: resizing, normalization, balancing, and augmentation. Six advanced DL algorithms—VGG16, Xception, DenseNet169, InceptionV3, MobileViT, and InceptionResNetV2—were applied for classification tasks. Among these, the DenseNet169 algorithm obtained the highest accuracy of 83.3% in ISBI2016 and 92.67% in ISBI2017. All models were then tested in an FL platform to maintain data privacy. In the FL platform, the VGG16 algorithm showed the best results, with 92.08% accuracy on ISBI2016 and 94% on ISBI2017. To ensure model interpretability, an XAI-based algorithm named Local Interpretable Model-Agnostic Explanations (LIME) was used to explain the predictions of the proposed framework. We believe the proposed framework offers a dependable tool for SC diagnosis while protecting sensitive medical data.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0324393.t005（source: https://plos.figshare.com/articles/dataset/Versions_of_the_core_libraries_used_in_this_work_/29584906）。
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。
⚡️ 需要数据支持或标注服务？立即联系我们获取专业方案。
