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An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease.

Doaa Sami Khafaga1, Abdelhameed Ibrahim2, El-Sayed M El-Kenawy3

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia.

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Summary
This summary is machine-generated.

A new computer-aided framework improves monkeypox diagnosis using deep convolutional neural networks (CNNs) and a novel Al-Biruni Earth radius (BER) optimization. This approach enhances accuracy in classifying monkeypox from skin disease images.

Keywords:
Al-Biruni Earth radiusdeep learningmeta-heuristicmonkeypox infectionoptimizationskin disease

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Area of Science:

  • Dermatology and Medical Imaging
  • Artificial Intelligence in Healthcare
  • Computational Biology

Background:

  • Human skin diseases, including monkeypox, are increasingly prevalent, posing significant health risks.
  • Manual diagnosis of monkeypox is challenging due to low image resolution, subjectivity, and time constraints.
  • Existing computer-aided diagnosis methods often rely on standard convolutional neural networks (CNNs) and classical loss functions.

Purpose of the Study:

  • To develop an automated computer-aided approach for accurate monkeypox disease diagnosis from skin images.
  • To enhance the classification performance of deep CNNs for monkeypox detection.
  • To introduce a novel optimization framework for improved feature learning in medical image analysis.

Main Methods:

  • A two-step approach involving deep CNNs for image embedding in Euclidean space.
  • Utilizing an optimized classification model with a triplet loss function for distinguishing between disease cases.
  • Employing the Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) to fine-tune deep CNN layers.
  • Training and testing the framework on a dataset of human skin disease images from an African hospital.

Main Results:

  • The proposed BERSFS-optimized CNN framework demonstrated superior performance in classifying monkeypox disease compared to existing methods.
  • Statistical validation using Wilcoxon and analysis of variance (ANOVA) tests confirmed the effectiveness and stability of the approach.
  • The method successfully learned discriminative features for distinguishing monkeypox from other skin conditions.

Conclusions:

  • The novel BERSFS optimization framework significantly enhances the accuracy and reliability of automated monkeypox diagnosis.
  • This computer-aided approach offers a promising solution to the limitations of manual diagnosis, improving diagnostic efficiency.
  • The study highlights the potential of advanced AI techniques in addressing global health challenges related to emerging infectious diseases.