MpoxMamba：一种用于猴痘检测的基于分组Mamba的轻量级混合网络
MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network for Mpox Detection
AI总结：
针对猴痘检测缺乏有效检测工具的问题，提出基于分组Mamba与深度可分离卷积的轻量级混合网络MpoxMamba，在保持0.77M参数量下性能超越现有轻量模型，还提供了在线检测应用与开源代码。
摘要：
由于缺乏有效的猴痘检测工具，猴痘病毒仍在全球范围内持续传播，且已再次被世界卫生组织宣布为国际关注的突发公共卫生事件。基于轻量级深度学习模型的检测系统对于缓解猴痘疫情暴发至关重要，因为它们适合大规模部署，尤其是在资源有限的场景中。然而，其成功应用的关键在于确保模型能够在保持轻量性的同时，有效建模猴痘皮损的局部特征和长程依赖关系。受Mamba在长程依赖建模方面的成功及其线性复杂度的启发，我们提出了一种名为MpoxMamba的轻量级混合架构，用于高效的猴痘检测。MpoxMamba利用深度可分离卷积提取猴痘皮肤皮损的局部特征表示，并通过分组Mamba模块极大增强了模型的全局上下文信息建模能力。值得注意的是，MpoxMamba的参数量和浮点运算量（FLOPs）分别为0.77M和0.53G。在两个广泛认可的基准数据集上的实验结果表明，MpoxMamba的性能优于当前最先进的轻量级模型以及现有的猴痘检测方法。重要的是，我们开发了一个基于网页的在线应用程序，提供免费的猴痘检测服务（http://5227i971s5.goho.co:30290）。MpoxMamba的源代码可在https://github.com/YubiaoYue/MpoxMamba获取。
英文摘要：
Due to the lack of effective mpox detection tools, the mpox virus continues to spread worldwide and has once again been declared a public health emergency of international concern by the World Health Organization. Lightweight deep learning model-based detection systems are crucial to alleviate mpox outbreaks since they are suitable for widespread deployment, especially in resource-limited scenarios. However, the key to its successful application depends on ensuring that the model can effectively model local features and long-range dependencies in mpox lesions while maintaining lightweight. Inspired by the success of Mamba in modeling long-range dependencies and its linear complexity, we proposed a lightweight hybrid architecture called MpoxMamba for efficient mpox detection. MpoxMamba utilizes depth-wise separable convolutions to extract local feature representations in mpox skin lesions and greatly enhances the model's ability to model the global contextual information by grouped Mamba modules. Notably, MpoxMamba's parameter size and FLOPs are 0.77M and 0.53G, respectively. Experimental results on two widely recognized benchmark datasets demonstrate that MpoxMamba outperforms state-of-the-art lightweight models and existing mpox detection methods. Importantly, we developed a web-based online application to provide free mpox detection (http://5227i971s5.goho.co:30290). The source codes of MpoxMamba are available at https://github.com/YubiaoYue/MpoxMamba.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
