融合机器学习与深度学习，无需数据增强即可高效分类正痘病毒图像。
Enhancing Orthopox Image Classification Using Hybrid Machine Learning and Deep Learning Models
- 用预训练深度模型提取特征，再结合传统机器学习分类。
- 在多个数据集上实现高准确率，且训练推理效率高。
- 适合医疗影像诊断场景，兼具可解释性与实际部署价值。
正痘病毒感染需通过医学影像进行精准分类，以实现早期诊断和疫情防控。传统诊断方法耗时且依赖专家判断，而正痘病毒各类别数据集稀缺且存在偏差。为提升分类性能并降低计算成本，本文提出一种混合策略：利用预训练深度学习模型提取深层特征表示，结合机器学习模型进行分类，无需数据增强。实验结果表明，该方法在多种主流算法中表现优异，兼顾分类精度与训练/推理效率。所提方案在多评估环境下展现强泛化能力与鲁棒性，为真实临床场景提供可扩展、可解释的自动化解决方案。
原文摘要 · Abstract (English)
Orthopoxvirus infections must be accurately classified from medical pictures for an easy and early diagnosis and epidemic prevention. The necessity for automated and scalable solutions is highlighted by the fact that traditional diagnostic techniques can be time-consuming and require expert interpretation and there are few and biased data sets of the different types of Orthopox. In order to improve classification performance and lower computational costs, a hybrid strategy is put forth in this paper that uses Machine Learning models combined with pretrained Deep Learning models to extract deep feature representations without the need for augmented data. The findings show that this feature extraction method, when paired with other methods in the state-of-the-art, produces excellent classification outcomes while preserving training and inference efficiency. The proposed approach demonstrates strong generalization and robustness across multiple evaluation settings, offering a scalable and interpretable solution for real-world clinical deployment.
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