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Related Experiment Video

Updated: Jul 12, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

AI-driven diagnosis of mpox using deep learning models.

Bassam W Aboshosha1, Shafiq Ul Rehman2, Lamees N Mahmoud1,3

  • 1School of Engineering and Computer Science, University of Hertfordshire, Hosted by Global Academic Foundation, New Administrative Capital, Egypt.

Plos One
|July 10, 2026
PubMed
Summary

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This study establishes a reliable benchmark for mpox image classification, comparing models on a unified dataset. A weighted ensemble model achieved the highest performance, offering a trustworthy reference for future research.

Area of Science:

  • Dermatology
  • Computer Science
  • Medical Imaging

Background:

  • Mpox lesions share visual similarities with other skin conditions, necessitating reliable image-based diagnostic tools.
  • Existing studies on mpox image classification are difficult to compare due to variations in dataset creation, augmentation strategies, and evaluation methods.
  • Developing a standardized and reproducible benchmark is crucial for advancing automated mpox detection.

Purpose of the Study:

  • To establish a leakage-aware benchmark for binary mpox classification.
  • To compare the performance of various pretrained deep learning models and a weighted ensemble.
  • To provide a trustworthy reference dataset and evaluation methodology for mpox image analysis.

Main Methods:

  • A unified dataset was created from MSLD v1.0 and v2.0, ensuring data integrity.

Related Experiment Videos

Last Updated: Jul 12, 2026

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data
09:47

DeepOmicsAE: Representing Signaling Modules in Alzheimer's Disease with Deep Learning Analysis of Proteomics, Metabolomics, and Clinical Data

Published on: December 15, 2023

  • Seven pretrained deep learning backbones and a weighted ensemble were evaluated using group-stratified five-fold cross-validation.
  • Evaluation included original-only testing, validation-based threshold selection, and temperature scaling for model calibration.

    • Main Results:

    • The weighted ensemble model achieved a mean accuracy of 0.8729, an F1-score of 0.8334, and an AUC of 0.9388.
    • ConvNeXt-Tiny demonstrated the strongest performance among single models, with an F1-score of 0.8159 and an AUC of 0.9284.
    • Results are presented as conservative, trustworthy reference values due to rigorous evaluation protocols.

    Conclusions:

    • The study provides a transparent and reproducible benchmark for mpox classification, emphasizing dataset curation and grouped evaluation.
    • The developed benchmark offers a reliable reference point, though external clinical validation is required for diagnostic deployment.
    • Methodological transparency and reproducible dataset curation are highlighted as key limitations and contributions to the field.