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Due to the lack of more efficient diagnostic tools for monkeypox, its spreadremains unchecked, presenting a formidable challenge to global health. Whilethe high efficacy of deep learning models for monkeypox diagnosis has beendemonstrated in related studies, the overlook of inference speed, the parametersize and diagnosis performance for early-stage monkeypox renders the modelsinapplicable in real-world settings. To address these challenges, we proposedan ultrafast and ultralight network named Fast-MpoxNet. Fast-MpoxNet possessesonly 0.27M parameters and can process input images at 68 frames per second(FPS) on the CPU. To counteract the diagnostic performance limitation broughtabout by the small model capacity, it integrates the attention-based featurefusion module and the multiple auxiliary losses enhancement strategy for betterdetecting subtle image changes and optimizing weights. Using transfer learningand five-fold cross-validation, Fast-MpoxNet achieves 94.26% Accuracy on theMpox dataset. Notably, its recall for early-stage monkeypox achieves 93.65%. Byadopting data augmentation, our model's Accuracy rises to 98.40% and attains aPracticality Score (A new metric for measuring model practicality in real-timediagnosis application) of 0.80. We also developed an application system namedMpox-AISM V2 for both personal computers and mobile phones. Mpox-AISM V2features ultrafast responses, offline functionality, and easy deployment,enabling accurate and real-time diagnosis for both the public and individualsin various real-world settings, especially in populous settings during theoutbreak. Our work could potentially mitigate future monkeypox outbreak andilluminate a fresh paradigm for developing real-time diagnostic tools in thehealthcare field.

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