提出工作的算法
基于深度学习、联邦学习和可解释AI的皮肤癌分类算法框架，在ISBI2016/2017数据集上验证。
基本信息
资源简介
提出工作的算法
【数据集背景】
本数据集源站标题为《Algorithm for the proposed 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 9.73 KB。Algorithm 1.xls：application/vnd.ms-excel，9.73 KB。数据集 DOI：10.1371/journal.pone.0324393.t013。
【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】
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/Algorithm_for_the_proposed_work_/29584894
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). Algorithm for the proposed work.. PLOS ONE. Dataset
下载信息
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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：Algorithm for the proposed work.
- DOI：10.1371/journal.pone.0324393.t013
- 发布日期：2025-07-16
- 文件数：1 个
- 文件总体积：9.73 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Algorithm_for_the_proposed_work_/29584894
命令行下载
curl -L -o "Algorithm 1.xls" "https://ndownloader.figshare.com/files/56328205"
文件清单
Algorithm 1.xls（9.73 KB）
文件总体积 9.73 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.t013（source: https://plos.figshare.com/articles/dataset/Algorithm_for_the_proposed_work_/29584894）。
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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