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Related Concept Videos

Smallpox01:24

Smallpox

Smallpox is a severe contagious disease caused by the Variola major virus, a double-stranded DNA member of the Poxviridae family.Variola major transmission occurs primarily via inhalation of virus-laden droplets or direct contact with infectious scabs. The incubation period averages approximately seven days, although it may range from 7 to 17 days depending on the inoculum and host factors.Clinically, the prodromal phase is marked by an abrupt onset of high fever, malaise, headache, and myalgia.

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DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
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Diagnosis of Monkeypox Disease Using Transfer Learning and Binary Advanced Dipper Throated Optimization Algorithm.

Amal H Alharbi1, S K Towfek2,3, Abdelaziz A Abdelhamid4,5

  • 1Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia.

Biomimetics (Basel, Switzerland)
|July 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new method using artificial intelligence to detect monkeypox skin lesions early. The approach enhances diagnostic accuracy, crucial for managing potential outbreaks and reducing public health concerns.

Keywords:
biological mechanismdeep learningdipper throated optimizationfeature selectionmonkeypox detectiontransfer learning

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Area of Science:

  • Medical Informatics
  • Computational Biology
  • Artificial Intelligence in Healthcare

Background:

  • Monkeypox virus (MPXV) detection is critical, especially following the COVID-19 pandemic, due to potential public health crises.
  • Skin lesions are a key indicator of MPXV infection, necessitating rapid and accurate diagnostic tools.
  • Existing diagnostic methods may require enhancement for pandemic-level detection capabilities.

Purpose of the Study:

  • To develop and evaluate a novel metaheuristic optimization approach for enhanced monkeypox detection.
  • To improve the performance of feature selection and classification for identifying monkeypox-indicative skin lesions.
  • To establish a robust system for rapid monkeypox case identification during potential pandemics.

Main Methods:

  • Utilized deep learning (GoogLeNet) and transfer learning for feature extraction from skin lesion indicators.
  • Employed a binary Dipper Throated Optimization (DTO) algorithm for efficient feature selection.
  • Applied a Decision Tree classifier, optimized with a continuous DTO algorithm, for accurate classification.

Main Results:

  • Achieved high performance metrics: F1-score of 0.92, sensitivity of 0.95, specificity of 0.61, p-Value of 0.89, and N-Value of 0.79.
  • The optimized Decision Tree classifier resulted in an overall accuracy of 94.35% for monkeypox detection.
  • Statistical tests (ANOVA, Wilcoxon signed rank test) confirmed the proposed method's superiority over alternatives.

Conclusions:

  • The proposed metaheuristic optimization approach significantly enhances the accuracy of monkeypox detection using skin lesions.
  • This AI-driven methodology offers a unique and valuable tool for rapid identification of monkeypox cases.
  • The findings support the potential of this approach for early detection and management of MPXV outbreaks.