MpoxMamba: A Grouped Mamba-based Lightweight Hybrid Network for Mpox Detection
Yubiao Yue, Jun Xue, Haihuang Liang, Zhenzhang Li, Yufeng Wang

TL;DR
MpoxMamba is a lightweight hybrid deep learning model that effectively detects mpox by modeling local features and long-range dependencies, outperforming existing models while being suitable for resource-limited settings.
Contribution
The paper introduces MpoxMamba, a novel lightweight hybrid network combining depth-wise separable convolutions and grouped Mamba modules for efficient mpox detection.
Findings
Outperforms state-of-the-art lightweight models on benchmark datasets.
Has a parameter size of only 0.77M and FLOPs of 0.53G.
Enables deployment in resource-limited scenarios.
Abstract
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…
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Taxonomy
TopicsPoxvirus research and outbreaks · Bacillus and Francisella bacterial research · Fractal and DNA sequence analysis
MethodsMamba: Linear-Time Sequence Modeling with Selective State Spaces
