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Deep Learning-Based Mpox Skin Lesion Detection and Real-Time Monitoring in a Smart Healthcare System.

Huda Alghoraibi1, Nuha Alqurashi1, Sarah Alotaibi1

  • 1Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|October 16, 2025
PubMed
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An AI system, ITMA'INN, accurately detects Mpox from skin lesion images using deep learning. This technology enhances public health responses through mobile diagnostics and real-time data analysis for smart cities.

Area of Science:

  • Artificial Intelligence in Healthcare
  • Medical Imaging Analysis
  • Public Health Informatics

Background:

  • Mpox poses a global health challenge, necessitating advanced diagnostic tools for effective public health surveillance.
  • Current diagnostic methods require improvement in scalability, accessibility, and accuracy.
  • The need for rapid and reliable detection of Mpox is critical for timely intervention.

Purpose of the Study:

  • To introduce ITMA'INN, an AI-driven system for Mpox detection from skin lesion images.
  • To develop a comprehensive healthcare system integrating AI diagnostics, mobile application, and public health dashboard.
  • To evaluate the performance of deep learning models for Mpox classification.

Main Methods:

  • Utilized transfer learning on public datasets to assess pretrained deep learning models.
Keywords:
AICNNMpox detectiondeep learningsmart healthcarevision transformer

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  • Developed an AI model pipeline, a cross-platform mobile application, and a real-time public health dashboard.
  • Evaluated binary and multiclass classification performance using metrics like accuracy and F1-score.

    • Main Results:

    • Peak accuracy of 97.8% achieved for binary Mpox vs. non-Mpox classification with models including Vision Transformer and MobileViT.
    • Multiclass classification accuracy reached 92% with ResNetViT and ViT Hybrid models for differentiating various skin conditions.
    • A mobile app utilizing MobileViT was developed for user analysis, symptom tracking, and healthcare center location.

    Conclusions:

    • ITMA'INN integrates AI diagnostics, mobile technology, and real-time analytics for responsive healthcare.
    • The system enhances public health infrastructure, particularly in smart city environments.
    • This AI-driven approach supports proactive public health management and disease surveillance.