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Artificial Intelligence Based COVID-19 Detection and Classification Model on Chest X-ray Images.

Turki Althaqafi1, Abdullah S Al-Malaise Al-Ghamdi1,2, Mahmoud Ragab3,4

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This summary is machine-generated.

This study introduces a novel AI model for rapid COVID-19 detection using chest X-rays. The sine cosine optimization with deep learning (SCODL-DDC) model accurately identifies COVID-19, improving diagnostic speed and efficiency.

Keywords:
COVID-19 diagnosischest X-raysdeep learningquantum neural networksine cosine algorithm

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Area of Science:

  • Medical Imaging and Artificial Intelligence
  • Infectious Disease Diagnostics
  • Computational Health

Background:

  • Accurate and timely diagnosis of COVID-19 is crucial for disease control and reducing mortality.
  • Chest X-ray (CXR) imaging offers a rapid, cost-effective, and accessible method for COVID-19 diagnosis.
  • While effective, CXR interpretation requires expert analysis, highlighting the need for automated solutions.

Purpose of the Study:

  • To develop and evaluate an automated deep learning (DL) based system for COVID-19 detection and classification using CXR images.
  • To introduce a novel Sine Cosine Optimization with Deep Learning-based Disease Detection and Classification (SCODL-DDC) technique.
  • To enhance diagnostic accuracy and efficiency in identifying COVID-19 from radiological scans.

Main Methods:

  • The proposed SCODL-DDC technique utilizes the EfficientNet model for feature extraction from CXR images.
  • Hyperparameter optimization for EfficientNet is performed using the Sine Cosine Optimization (SCO) algorithm.
  • A Quantum Neural Network (QNN) model is employed for accurate COVID-19 classification, with parameters optimized by the Equilibrium Optimizer (EO).

Main Results:

  • The SCODL-DDC technique demonstrated superior performance in detecting and classifying COVID-19 from CXR images compared to existing methods.
  • The integration of SCO for hyperparameter tuning and EO for QNN parameter optimization led to enhanced diagnostic accuracy.
  • The study validates the effectiveness of AI-driven approaches in medical image analysis for infectious diseases.

Conclusions:

  • The SCODL-DDC technique presents a highly effective and automated approach for COVID-19 diagnosis using CXR images.
  • The proposed method offers a promising tool for healthcare professionals to improve early detection and management of COVID-19.
  • This AI-powered diagnostic system has the potential to significantly impact public health strategies during pandemics.