猴痘皮肤病变数据集
包含猴痘、水痘和麻疹皮肤病变图像的数据集，用于深度学习自动检测猴痘。
基本信息
资源简介
该数据集包含猴痘、水痘和麻疹的皮肤病变图像，用于训练和评估基于深度学习的猴痘自动检测框架，旨在通过分析皮肤病变图像提高早期诊断的准确性和效率。
下载信息
注册下载
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暂未开放公开下载
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使用方式
数据集说明
猴痘皮肤病变数据集 对应论文数据集（arXiv 预印本）。
数据获取指引
- 打开论文页面获取作者与项目信息：https://arxiv.org/abs/2504.17540v1
- 论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
- 获取到实际数据链接后，按对应平台标准方式下载。
论文摘要：Abstract:The recent global spread of monkeypox, particularly in regions where it has not historically been prevalent, has raised significant public health concerns. Early and accurate diagnosis is critical for effective disease management and control. In response, this study proposes a novel deep learning-based framework for the automated detection of monkeypox from skin lesion images, leveraging the power of transfer learning, dimensionality reduction, and advanced machine learning techniques. We utilize the newly developed Monkeypox Skin Lesion Dataset (MSLD), which includes images of monkeypox, chickenpox, and measles, to train and evaluate our models. The proposed framework employs the Xception architecture for deep feature extraction, followed by Principal Component Analysis (PCA) for dimensionality reduction, and the Natural Gradient Boosting (NGBoost) algorithm for classification. To optimize the model's performance and generalization, we introduce the African Vultures Optimization Algorithm (AVOA) for hyperparameter tuning, ensuring efficient exploration of the parameter space. Our results demonstrate that the proposed AVOA-NGBoost model achieves state-of-the-art performance, with an accuracy of 97.53%, F1-score of 97.72% and an AUC of 97.47%. Additionally, we enhance model interpretability using Grad-CAM and LIME techniques, providing insights into the decision-making process and highlighting key features influencing classification. This framework offers a highly precise and efficient diagnostic tool, potentially aiding healthcare providers in early detection a
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