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Enhancing multiclass COVID-19 prediction with ESN-MDFS: Extreme smart network using mean dropout feature selection

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A new AI model, Enhancing COVID Prediction with ESN-MDFS, accurately diagnoses lung conditions like COVID-19 and pneumonia from chest X-rays. This deep learning approach significantly improves diagnostic accuracy and efficiency for portable X-ray analysis.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Pulmonology

Background:

  • Portable chest X-rays (CXRs) are crucial for diagnosing lung conditions.
  • Deep learning (DL) and artificial intelligence (AI) show potential in enhancing CXR analysis.
  • Accurate differentiation of various lung pathologies, including COVID-19, bacterial, and viral pneumonia, remains a challenge.

Purpose of the Study:

  • To develop and evaluate a novel AI model for multi-class lung condition detection in portable CXRs.
  • To improve the diagnostic accuracy and efficiency of identifying COVID-19, bacterial pneumonia, viral pneumonia, and normal cases.
  • To combine static texture features with dynamic deep learning features for enhanced classification.

Main Methods:

  • Utilized a dataset of over 6,000 portable CXR images, including COVID-19, normal, viral pneumonia, and bacterial pneumonia cases.
  • Developed the "Enhancing COVID Prediction with ESN-MDFS" model, integrating an Extreme Smart Network (ESN) and Mean Dropout Feature Selection Technique (MDFS).
  • Employed a pre-trained VGG-16 model for feature extraction, combined with static texture features, and addressed data imbalance and hyperparameter tuning.

Main Results:

  • The ESN-MDFS model achieved a peak accuracy of 96.18% and an Area Under the Curve (AUC) of 1.00.
  • Demonstrated superior performance in differentiating between COVID-19, bacterial pneumonia, viral pneumonia, and normal conditions.
  • Six-fold cross-validation confirmed the model's robust and reliable diagnostic capabilities.

Conclusions:

  • The proposed ESN-MDFS model offers a significant advancement in the automated diagnosis of lung conditions using portable CXRs.
  • This AI-driven approach promises to enhance diagnostic accuracy and efficiency, aiding clinicians in timely patient management.
  • The study highlights the potential of integrating DL and AI for improved lung condition detection in resource-limited settings.