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Generative adversarial network based data augmentation for CNN based detection of Covid-19.

Rutwik Gulakala1, Bernd Markert1, Marcus Stoffel2

  • 1Institute of General Mechanics, RWTH Aachen University, Aachen, Germany.

Scientific Reports
|November 10, 2022
PubMed
Summary

This study introduces an AI-powered tool for rapid Covid-19 diagnosis using chest X-rays. The novel method achieves 99.2% accuracy, offering an accessible and efficient diagnostic solution for lung infections.

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Computer Vision

Background:

  • Covid-19 diagnosis faces challenges in accessibility and speed.
  • Medical imaging, particularly chest X-rays (CXR), offers a widely available diagnostic avenue.
  • Supervised learning for image analysis requires extensive training data, which can be a limitation.

Purpose of the Study:

  • To develop a rapid and accurate AI-based diagnostic tool for Covid-19 detection using chest X-ray images.
  • To address the limitations of data scarcity and computational inefficiency in existing deep learning models for medical image analysis.
  • To create synthetic and augmented data for training AI models, improving their generalization capabilities.

Main Methods:

  • A novel Generative Adversarial Network (GAN) architecture (Swish activated, Instance and Batch normalized Residual U-Net GAN) was developed for synthetic data generation.

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Lung CT Segmentation to Identify Consolidations and Ground Glass Areas for Quantitative Assesment of SARS-CoV Pneumonia
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  • A lightweight Convolutional Neural Network (CNN) architecture, 40% lighter than state-of-the-art models, was proposed for efficient image analysis.
  • Multi-class classification of chest X-rays (CXR) into Covid-19, healthy, and Pneumonia categories was performed.

    • Main Results:

    • The proposed GAN architecture effectively generates realistic synthetic X-ray data, handling variations in image luminosity.
    • The novel CNN model achieved a highly accurate multi-class classification with a test accuracy of 99.2% for Covid-19 detection.
    • The developed AI method provides a rapid diagnostic tool with high accuracy for lung infection identification.

    Conclusions:

    • The AI-based diagnostic tool offers a promising solution for rapid and accessible Covid-19 identification using chest X-rays.
    • The combination of GANs for data augmentation and a lightweight CNN for classification addresses key challenges in medical AI.
    • This approach has the potential to significantly support clinical decision-making in diagnosing Covid-19 and other lung conditions.