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DeepGenMon: A Novel Framework for Monkeypox Classification Integrating Lightweight Attention-Based Deep Learning and

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  • 1Department of Computer Science, College of Computer Science and Engineering, Taibah University, Yanbu 46421, Saudi Arabia.

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Summary
This summary is machine-generated.

This study introduces DeepGenMon, a lightweight AI framework for accurate monkeypox and skin disease detection. It offers high precision and recall with reduced computational needs, making it ideal for low-resource settings.

Keywords:
CNNattention mechanismdeep learninggenetic algorithmsmonkeypoxpandemic

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Area of Science:

  • Artificial Intelligence in Dermatology
  • Medical Image Analysis
  • Computational Biology

Background:

  • The global spread of monkeypox necessitates improved diagnostic tools for public health.
  • Existing AI models for skin disease classification, including monkeypox, often require substantial computational resources.
  • There is a need for efficient and accurate AI solutions for early detection and classification of skin conditions.

Purpose of the Study:

  • To propose a novel, lightweight framework, DeepGenMon, for accurate classification of various skin diseases, including monkeypox.
  • To enhance detection accuracy and optimize model hyperparameters using an attention-based CNN and a genetic algorithm (GA).
  • To develop a resource-efficient AI model suitable for low-resource environments.

Main Methods:

  • Developed DeepGenMon, integrating an attention mechanism with a Convolutional Neural Network (CNN) for feature highlighting.
  • Employed a genetic algorithm (GA) to optimize the CNN's hyperparameters, improving robustness and classification accuracy.
  • Evaluated the framework on two public datasets comprising diverse skin disease images.

Main Results:

  • DeepGenMon achieved superior performance over state-of-the-art models, with high precision, recall, and F-score (e.g., 0.985 on dataset 1).
  • The model demonstrated significantly lower computational resource requirements and faster inference times (e.g., 2.1753 s on dataset 2).
  • Achieved high accuracy in classifying various skin conditions, including monkeypox, chickenpox, and melasma.

Conclusions:

  • DeepGenMon is an effective and efficient AI tool for the accurate classification of diverse skin diseases.
  • Its lightweight design and high performance make it a promising solution for clinical settings, especially in resource-limited areas.
  • The integration of attention mechanisms and genetic algorithms offers a robust approach to medical image analysis.