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Automated COVID-19 Classification Using Heap-Based Optimization with the Deep Transfer Learning Model.

Bahjat Fakieh1, Mahmoud Ragab2,3,4

  • 1Information Systems Department, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.

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This study introduces a novel AI model, HBODTL-DC, for rapid COVID-19 detection using chest X-rays. The model achieves high accuracy, aiding in faster diagnosis and disease control.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Deep Learning

Background:

  • COVID-19 pandemic requires rapid identification of infected individuals.
  • Radiological imaging (CT, CXR) aids COVID-19 diagnosis but is time-consuming and requires expertise.
  • Artificial intelligence (AI) and deep learning (DL) show promise in analyzing medical images for COVID-19 assessment.

Purpose of the Study:

  • To develop and evaluate a novel deep transfer learning model, HBODTL-DC, for efficient COVID-19 detection and classification using chest X-ray (CXR) images.
  • To enhance image quality and extract relevant features for accurate classification of COVID-19 cases from CXR scans.

Main Methods:

  • The HBODTL-DC model utilizes Gabor filtering (GF) for image enhancement.
  • Feature extraction is performed using a neural architecture search network (NasNet) large model combined with a heap-based optimization (HBO) algorithm.
  • Classification is conducted using an Elman Neural Network (ENN) model.

Main Results:

  • The HBODTL-DC model demonstrated superior performance in identifying COVID-19 on CXR images.
  • Experimental validation on a Kaggle CXR dataset yielded a maximum accuracy of 0.9992.
  • The proposed model outperformed existing approaches in COVID-19 detection accuracy.

Conclusions:

  • The HBODTL-DC model offers a highly accurate and efficient AI-driven solution for COVID-19 detection from CXR images.
  • This approach can assist healthcare professionals in faster diagnosis and management of COVID-19.
  • The study highlights the potential of integrated AI techniques for improving infectious disease diagnostics.