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Advanced Cognitive Algorithm for Biomedical Data Processing: COVID-19 Pattern Recognition as a Case Study.

Mohamed Elhoseny1,2, Zahraa Tarek2, Ibrahim M El-Hasnony2

  • 1College of Computing and Informatics, University of Sharjah, Sharjah, UAE.

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|April 1, 2022
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Summary
This summary is machine-generated.

This study introduces an artificial neural network (ANN) model optimized with a butterfly algorithm for accurate COVID-19 detection from chest X-rays and CT scans. The hybrid model achieved superior performance in identifying COVID-19 patterns compared to other methods.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computational Biology

Background:

  • Automated disease prediction is crucial for managing public health crises like COVID-19.
  • Accurate and rapid diagnosis of COVID-19 is essential to reduce mortality rates.
  • Challenges in COVID-19 diagnosis include viral mutation and the need for intelligent detection systems.

Purpose of the Study:

  • To develop and evaluate a hybrid artificial neural network (ANN) model for automated COVID-19 detection using chest X-ray (CXR) and computerized tomography (CT) images.
  • To optimize the ANN model's parameters using the butterfly optimization algorithm (BOA).
  • To compare the proposed model's performance against established methods like AlexNet, GoogLeNet, and Support Vector Machine (SVM).

Main Methods:

  • A hybrid model combining an artificial neural network (ANN) with parameter optimization via the butterfly optimization algorithm (BOA) was proposed.
  • The model was trained and tested on six publicly available datasets comprising CXR and CT images.
  • Performance was evaluated by comparing the proposed model against pretrained AlexNet, GoogLeNet, and SVM.

Main Results:

  • The proposed hybrid ANN-BOA model demonstrated superior performance in recognizing COVID-19 patterns from medical images.
  • The model achieved an average accuracy of 90.48%, outperforming SVM (81.09%), AlexNet (86.76%), and GoogLeNet (84.97%).
  • The experimental results validated the model's effectiveness across both X-ray and CT imaging datasets.

Conclusions:

  • The hybrid ANN-BOA model offers a highly accurate and effective approach for automated COVID-19 detection from CXR and CT scans.
  • This intelligent detection system can significantly aid physicians in diagnosing COVID-19, leading to faster outcomes and potentially lower mortality rates.
  • The study highlights the potential of optimized deep learning models in medical diagnostics for infectious diseases.