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An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification.

Usman Asghar1, Muhammad Arif1, Khurram Ejaz1

  • 1Department of Computer Science & Information Technology, The University of Lahore, Pakistan.

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This study enhances COVID-19 detection using deep learning and data augmentation. A novel QuNet model achieved 90% accuracy with GAN-based augmentation, improving upon existing methods for faster diagnosis.

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Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Deep Learning

Background:

  • Coronavirus disease (COVID-19), caused by SARS-CoV-2, is a fatal pandemic with significant global mortality.
  • Current COVID-19 detection methods are time-consuming, exacerbating the pandemic's impact.
  • Deep learning models for COVID-19 diagnosis face challenges due to limited public datasets, leading to model overfitting.

Purpose of the Study:

  • To investigate the efficacy of artificial intelligence-based data augmentation techniques for improving COVID-19 detection.
  • To evaluate existing deep convolutional neural networks (DenseNet-121, InceptionV3, Xception, ResNet101) and propose a novel network (QuNet) for COVID-19 detection using augmented datasets.
  • To compare the performance of classical and Generative Adversarial Network (GAN)-based data augmentation methods.

Main Methods:

  • Employed classical and GAN-based data augmentation techniques to expand the COVID-19 X-ray image dataset.
  • Trained and evaluated four established deep convolutional networks (DenseNet-121, InceptionV3, Xception, ResNet101) on the augmented datasets.
  • Developed and assessed a novel convolutional neural network, QuNet, for enhanced COVID-19 detection accuracy.

Main Results:

  • Both QuNet and Xception demonstrated high accuracy when trained on classically augmented datasets.
  • QuNet significantly outperformed other models, achieving 90% detection accuracy with GAN-based augmented data.
  • Data augmentation, particularly GAN-based methods, proved effective in mitigating model overfitting and improving diagnostic performance.

Conclusions:

  • Deep learning models, especially the proposed QuNet, combined with advanced data augmentation, offer a promising approach for accurate and efficient COVID-19 detection from X-ray images.
  • GAN-based augmentation shows superior performance in enhancing model generalization and achieving high diagnostic accuracy.
  • The findings suggest a potential for AI-driven tools to aid in rapid pandemic response and clinical decision-making.