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Updated: Nov 10, 2025

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Hyperparameter Optimization for COVID-19 Pneumonia Diagnosis Based on Chest CT.

Paulo Lacerda1, Bruno Barros1, Célio Albuquerque1

  • 1Institute of Computing, Fluminense Federal University, Niteroi, RJ 24.310-346, Brazil.

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Summary

Hyperband optimization algorithm enhanced Convolutional Neural Network (CNN) performance for COVID-19 diagnosis from CT scans. The optimized CNN achieved superior accuracy compared to RT-PCR tests and human experts.

Keywords:
COVID-19computer-aided diagnosisdeep learninghyperparameter optimization

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computational Biology

Background:

  • Convolutional Neural Networks (CNNs) show promise in medical diagnosis.
  • Optimizing CNN architecture and hyperparameters is challenging for disease detection.

Purpose of the Study:

  • Investigate Hyperband optimization for CNNs in COVID-19 diagnosis.
  • Optimize CNN hyperparameters including backbone, inception modules, neurons, and learning rate.

Main Methods:

  • Utilized the Optuna framework with the Hyperband algorithm.
  • Trained CNN models on 2175 computed tomography (CT) images for SARS-CoV-2 detection.
  • Optimized four key hyperparameters: backbone architecture, inception modules, fully connected layer neurons, and learning rate.

Main Results:

  • Identified an optimal VGG16 CNN architecture with specific hyperparameter settings.
  • Achieved high diagnostic performance: 97% sensitivity, 82% precision, and 88% accuracy.
  • Outperformed Real-Time Polymerase Chain Reaction (RT-PCR) sensitivity (53-88%) and human expert accuracy (72%).

Conclusions:

  • Hyperband optimization effectively enhances CNNs for COVID-19 diagnosis using CT images.
  • The optimized CNN model offers a more accurate diagnostic tool than current methods.
  • This approach holds potential for improving medical diagnostic capabilities through automated model optimization.