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

TL;DR

This paper introduces Fast-MpoxNet, a highly efficient deep learning model with minimal parameters and high speed, enabling real-time mpox diagnosis on various devices, thus aiding early detection and outbreak control.

Contribution

The development of Fast-MpoxNet, an ultrafast, ultralight deep learning model with attention modules and auxiliary losses, optimized for real-time mpox diagnosis on CPU and mobile devices.

Findings

Fast-MpoxNet achieves 98.40% accuracy on mpox classification.

It processes images at 68 FPS on CPU, enabling real-time diagnosis.

Recall for early-stage mpox is 93.65%.

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 0.27M 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.