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Robustly detecting mpox and non-mpox using a deep learning framework based on image inpainting.

Yujun Cao1, Yubiao Yue2, Xiaoming Ma1

  • 1Department of Basic Courses, Guangzhou Maritime University, Guangzhou, 510725, China.

Scientific Reports
|January 10, 2025
PubMed
Summary

A new deep learning method, Mask, Inpainting, and Measure (MIM), effectively detects mpox (monkeypox) from skin images. This approach improves accuracy and handles noisy or unusual inputs, aiding public health efforts.

Keywords:
Deep learningGenerative modelImage InpaintingMpox DetectionNovelty detection

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Dermatology

Background:

  • Mpox (monkeypox) diagnosis lacks efficient technology, hindering case management.
  • Deep learning shows promise for mpox detection but faces challenges with real-world data noise and abnormal inputs.

Purpose of the Study:

  • To develop a robust deep learning strategy for accurate mpox detection.
  • To overcome limitations of existing methods, including noise sensitivity and inability to detect unknown conditions.

Main Methods:

  • Proposed a novel "Mask, Inpainting, and Measure" (MIM) strategy utilizing generative adversarial networks.
  • MIM learns mpox image features through inpainting masked images and measures similarity to detect mpox versus non-mpox cases.

Main Results:

  • MIM demonstrated effectiveness and robustness on mpox and non-mpox skin disease datasets.
  • Achieved an average Area Under the Receiver Operating Characteristic Curve (AUROC) of 0.8237.
  • External clinical testing validated MIM's robustness.

Conclusions:

  • MIM offers a superior approach to mpox detection compared to traditional classification models.
  • The method effectively handles unknown categories and abnormal inputs, suitable for practical application.
  • A free smartphone app was developed to facilitate convenient mpox detection for the public and healthcare professionals.