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Steps in Outbreak Investigation01:18

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Pandemic disease detection through wireless communication using infrared image based on deep learning.

Mohammed Alhameed1, Fathe Jeribi1, Bushra Mohamed Elamin Elnaim2

  • 1College of CS & IT, Jazan University, Jazan, Saudi Arabia.

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Rapid disease diagnosis is crucial. This study introduces an AI system using infrared imaging and machine learning for faster, more accurate pandemic detection, improving upon traditional PCR tests.

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Area of Science:

  • Medical Diagnostics
  • Artificial Intelligence
  • Machine Learning

Background:

  • Traditional reverse transcriptase-polymerase chain reaction (R.T.-PCR) tests for diseases like COVID-19 are time-consuming and prone to false negatives.
  • Shortages of R.T.-PCR test kits highlight the need for rapid and accurate alternative diagnostic methods.
  • Early detection of pandemic diseases is critical, but initial radiological findings may not be immediately apparent.

Purpose of the Study:

  • To develop an Artificial Intelligence (A.I.)-based Machine Intelligence (MI) system for rapid and accurate diagnosis of pandemic diseases.
  • To integrate infrared imaging for initial temperature measurement with other patient data (C.T. scans, symptoms, history) for comprehensive analysis.
  • To enhance diagnostic efficiency and accuracy, addressing limitations of current pandemic testing protocols.

Main Methods:

  • Utilized infrared cameras to capture facial temperature data across eight classes and twelve regions, storing information in a 'patient-info-mask' database.
  • Implemented a cloudlets server with a wireless network for accessible data processing and minimal infrastructure requirements.
  • Employed deep learning, specifically Convolutional Neural Networks (CNNs), for data verification and incorporated a tenfold cross-validation method with a decision tree level synthesis method.

Main Results:

  • The proposed A.I. system integrates diverse patient data for a holistic diagnostic approach.
  • Achieved a 3.29% increase in accuracy by combining the decision tree level synthesis method and ten-folded-validation.
  • Demonstrated enhanced accuracy and efficiency in disease estimation compared to traditional methods.

Conclusions:

  • The developed A.I. and Machine Intelligence system offers a robust and efficient solution for rapid pandemic disease diagnosis.
  • Infrared imaging combined with deep learning provides a promising avenue for early disease detection.
  • The proposed method significantly improves diagnostic accuracy, addressing critical needs during pandemics.