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This study introduces an AI model using deep learning on chest X-rays to detect COVID-19 pneumonia with 98% accuracy. The AI distinguishes COVID-19 pneumonia from regular pneumonia, improving diagnosis beyond RT-PCR limitations.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pneumonia Diagnosis

Background:

  • The COVID-19 pandemic strained healthcare systems globally.
  • Reverse transcription-polymerase chain reaction (RT-PCR) has limitations in COVID-19 detection accuracy (70%).
  • COVID-19 commonly causes pneumonia, making chest X-rays a potential diagnostic tool.

Purpose of the Study:

  • To develop and evaluate an AI model for detecting COVID-19-induced pneumonia from chest X-rays.
  • To differentiate COVID-19 pneumonia from regular pneumonia using deep learning.
  • To improve diagnostic accuracy beyond traditional methods.

Main Methods:

  • Utilized Convolutional Neural Network (CNN) and deep learning techniques.
  • Employed transfer learning with fine-tuning using Xception, VGG16, and VGG19 models.
  • Classified chest X-rays into three categories: COVID-19 pneumonia, regular pneumonia, and normal.

Main Results:

  • Achieved a 98% accuracy in detecting COVID-19-induced pneumonia.
  • Demonstrated high performance across various metrics including precision, recall, and F1 score.
  • Successfully distinguished between COVID-19 pneumonia and common pneumonia, addressing diagnostic overlap.

Conclusions:

  • The proposed AI model shows significant promise for accurate COVID-19 pneumonia detection from chest X-rays.
  • This approach can aid in differentiating lung infections, offering a more reliable diagnostic standard.
  • The AI model can help mitigate diagnostic complexities and improve patient outcomes.