  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 0.27M 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.
