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Innovative Hybrid CNN-Transformer Deep Learning Models For The Automated Diagnosis Of Monkeypox From Medical Images.

Bhawani Sankar Panigrahi1, R Kishore Kanna2, Nukala Sujata Gupta3

  • 1Department of Computer Science & System Engineering, GITAM School of CSE, GITAM University.

Journal of Visualized Experiments : Jove
|June 15, 2026
PubMed
Summary

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This summary is machine-generated.

A hybrid deep-learning model effectively classifies monkeypox (mpox) images, achieving high precision and recall. This AI approach aids in distinguishing mpox from similar skin conditions, improving diagnostic accuracy.

Area of Science:

  • Dermatology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • The visual diagnosis of monkeypox (mpox) lesions is challenging due to similarities with other vesicular and pustular skin diseases.
  • Accurate and timely classification of mpox is crucial for effective public health response and patient management.

Purpose of the Study:

  • To evaluate a hybrid deep-learning model integrating convolutional neural networks (CNNs) and transformers for automated mpox image classification.
  • To compare the performance of the hybrid model against various established deep learning architectures.

Main Methods:

  • A benchmark dataset of 2,280 mpox and non-mpox lesion images was utilized.
  • Images were preprocessed and augmented. A hybrid CNN-transformer model was developed and compared with Sequential CNN, InceptionV3, ResNetV2, ResNet50, and DenseNet121.

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  • Models were trained for 15 epochs using the Adam optimizer and binary cross-entropy loss. Performance metrics included accuracy, precision, recall, F1-score, and AUC.

    • Main Results:

    • The hybrid CNN-transformer model demonstrated a balanced discriminative profile with 98.50% precision, 98.50% recall, and 98.58% F1-score.
    • While a Sequential CNN achieved the highest internal validation accuracy, the hybrid model showed strong performance.
    • Some baseline models like InceptionV3 exhibited overfitting, and ResNetV2 lacked sufficient validation data for robust generalization testing.

    Conclusions:

    • The hybrid deep-learning model presents a viable and balanced strategy for automated mpox image classification.
    • This AI-driven approach can assist in differentiating mpox from visually similar dermatological conditions.
    • Further validation may be needed to confirm if the hybrid model consistently outperforms all baseline models across diverse datasets.