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Artificial intelligence-powered image analysis: A paradigm shift in infectious disease detection.

Muhammad Ahsan1, Robertas Damaševičius1

  • 1Faculty of Informatics, Vytautas Magnus University, Kaunas, Lithuania.

Artificial Intelligence in Medicine
|November 28, 2024
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Summary

This study introduces an AI-driven mathematical model using Multi-Criteria Decision-Making and Hypersoft Sets to improve infectious disease diagnosis from medical images. This approach enhances diagnostic accuracy and aids in determining appropriate patient management strategies.

Keywords:
Infectious diseasesMachine learningMedical images

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Area of Science:

  • Medical Imaging Analysis
  • Artificial Intelligence in Diagnostics
  • Infectious Disease Management

Background:

  • Global infectious diseases pose significant mortality risks.
  • Varied symptoms complicate accurate infection assessment and severity determination.
  • Diverse challenges exist for different countries in managing infectious diseases.

Purpose of the Study:

  • To introduce innovative Artificial Intelligence (AI) based methodologies for enhanced diagnostic accuracy.
  • To develop a mathematical model for identifying infectious diseases from medical imagery.
  • To integrate a Multi-Criteria Decision-Making (MCDM) framework with Hypersoft Set (HSS) within a fuzzy context for AI-driven diagnostics.

Main Methods:

  • Development of an AI-based mathematical model for image analysis.
  • Application of a Multi-Criteria Decision-Making (MCDM) framework.
  • Integration of Hypersoft Set (HSS) within a fuzzy context for enhanced decision-making.

Main Results:

  • The proposed AI model demonstrates potential for accurate identification of infectious diseases from medical images.
  • The MCDM framework combined with HSS provides a novel approach to AI-driven diagnostic processes.
  • The study highlights the effectiveness of visual aids in understanding and validating the proposed methods.

Conclusions:

  • The developed AI methodologies offer a significant advancement in the diagnostic accuracy of infectious diseases.
  • This approach has potential for widespread application in machine learning, deep learning, and pattern recognition.
  • The study represents a stride in combating infectious diseases through advanced diagnostic techniques.