The global outbreak of Mpox virus, classified as a Public Health Emergency of International Concern by WHO, presents significant diagnostic challenges due to its visual similarity to other skin lesion diseases. Current clinical detection techniques face limitations in accuracy and efficiency, necessitating improved automated diagnostic solutions. This study introduces a novel Cascaded Atrous Group Attention (CAGA) module, specifically designed to enhance multi-scale feature representation while optimizing computational efficiency. By integrating CAGA with EfficientViT-L1 as the backbone architecture, our approach achieves state-of-the-art performance with a score of 0.98% on the MCSI dataset, while reducing model parameters by 37.5% compared to the original EfficientViT-L1. This reduction in computational complexity maintains diagnostic accuracy while enabling broader deployment across resource-constrained healthcare settings. Extensive validation across two other benchmark datasets, including MSID and MSLD, demonstrate the model's robustness, consistently outperforming existing approaches. Our findings suggest that CAGA's efficient feature extraction mechanism could be adapted for other medical imaging tasks requiring fine-grained visual discrimination.

翻译：世界卫生组织将Mpox病毒的全球爆发列为国际关注的突发公共卫生事件，由于其与其他皮肤病变疾病的视觉相似性，带来了重大的诊断挑战。当前临床检测技术在准确性和效率方面存在局限，亟需改进的自动化诊断解决方案。本研究提出了一种新颖的级联空洞分组注意力模块，专门设计用于增强多尺度特征表示，同时优化计算效率。通过将CAGA模块与作为主干架构的EfficientViT-L1相结合，我们的方法在MCSI数据集上取得了0.98%分数的先进性能，同时相较于原始EfficientViT-L1减少了37.5%的模型参数量。这种计算复杂度的降低在保持诊断准确性的同时，使得模型能够在资源受限的医疗环境中更广泛地部署。在包括MSID和MSLD在内的另外两个基准数据集上的广泛验证证明了该模型的鲁棒性，其性能持续优于现有方法。我们的研究结果表明，CAGA的高效特征提取机制可适用于其他需要细粒度视觉辨别的医学影像任务。