Ultrafast-and-Ultralight ConvNet-Based Intelligent Monitoring System for Diagnosing Early-Stage Mpox Anytime and Anywhere

IF 6.8 Q1 AUTOMATION & CONTROL SYSTEMS Advanced intelligent systems (Weinheim an der Bergstrasse, Germany) Pub Date : 2024-06-10 DOI:10.1002/aisy.202300637
Yubiao Yue, Xiaoqiang Shi, Li Qin, Xinyue Zhang, Jialong Xu, Zipei Zheng, Zhenzhang Li, Yang Li
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Abstract

Due to the absence of more efficient diagnostic tools, the spread of mpox continues to be unchecked. Although related studies have demonstrated the high efficiency of deep learning models in diagnosing mpox, key aspects such as model inference speed and parameter size have always been overlooked. Herein, an ultrafast and ultralight network named Fast-MpoxNet is proposed. Fast-MpoxNet, with only 0.27 m parameters, can process input images at 68 frames per second (FPS) on the CPU. To detect subtle image differences and optimize model parameters better, Fast-MpoxNet incorporates an attention-based feature fusion module and a multiple auxiliary losses enhancement strategy. Experimental results indicate that Fast-MpoxNet, utilizing transfer learning and data augmentation, produces 98.40% classification accuracy for four classes on the mpox dataset. Furthermore, its Recall for early-stage mpox is 93.65%. Most importantly, an application system named Mpox-AISM V2 is developed, suitable for both personal computers and smartphones. Mpox-AISM V2 can rapidly and accurately diagnose mpox and can be easily deployed in various scenarios to offer the public real-time mpox diagnosis services. This work has the potential to mitigate future mpox outbreaks and pave the way for developing real-time diagnostic tools in the healthcare field.

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基于 ConvNet 的超快超轻智能监测系统可随时随地诊断早期麻风病
由于缺乏更高效的诊断工具,麻腮风的蔓延仍未得到遏制。尽管相关研究已经证明了深度学习模型在诊断痘病方面的高效性,但模型推理速度和参数大小等关键环节一直被忽视。在此,我们提出了一种名为 Fast-MpoxNet 的超快超轻网络。Fast-MpoxNet 的参数仅为 0.27 m,可在 CPU 上以每秒 68 帧(FPS)的速度处理输入图像。为了检测图像的细微差别并更好地优化模型参数,Fast-MpoxNet 采用了基于注意力的特征融合模块和多重辅助损失增强策略。实验结果表明,利用迁移学习和数据增强,Fast-MpoxNet 在 mpox 数据集上的四类分类准确率达到 98.40%。此外,它对早期 mpox 的召回率为 93.65%。最重要的是,我们开发了一个名为 Mpox-AISM V2 的应用系统,它既适用于个人电脑,也适用于智能手机。Mpox-AISM V2 可快速、准确地诊断天花,并可轻松部署在各种场景中,为公众提供实时天花诊断服务。这项工作有望缓解未来的天花疫情,并为开发医疗保健领域的实时诊断工具铺平道路。
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1.30
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审稿时长
4 weeks
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