CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection (2103.05094v1)

Abstract: Coronavirus (COVID-19) is a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The spread of COVID-19 seems to have a detrimental effect on the global economy and health. A positive chest X-ray of infected patients is a crucial step in the battle against COVID-19. Early results suggest that abnormalities exist in chest X-rays of patients suggestive of COVID-19. This has led to the introduction of a variety of deep learning systems and studies have shown that the accuracy of COVID-19 patient detection through the use of chest X-rays is strongly optimistic. Deep learning networks like convolutional neural networks (CNNs) need a substantial amount of training data. Because the outbreak is recent, it is difficult to gather a significant number of radiographic images in such a short time. Therefore, in this research, we present a method to generate synthetic chest X-ray (CXR) images by developing an Auxiliary Classifier Generative Adversarial Network (ACGAN) based model called CovidGAN. In addition, we demonstrate that the synthetic images produced from CovidGAN can be utilized to enhance the performance of CNN for COVID-19 detection. Classification using CNN alone yielded 85% accuracy. By adding synthetic images produced by CovidGAN, the accuracy increased to 95%. We hope this method will speed up COVID-19 detection and lead to more robust systems of radiology.

Overview of CovidGAN: Data Augmentation for Enhanced COVID-19 Detection

This paper presents an innovative approach to address the challenges of limited datasets in medical imaging, particularly concerning COVID-19 detection using chest X-rays (CXR). The authors introduce CovidGAN, an Auxiliary Classifier Generative Adversarial Network (ACGAN)-based model designed to generate synthetic CXR images, thereby augmenting existing datasets to improve the accuracy of COVID-19 detection using Convolutional Neural Networks (CNNs).

Methodology

The research leverages the principles of Generative Adversarial Networks (GANs) to synthesize realistic CXR images. GANs, and specifically the ACGAN variant, employ two neural networks, a generator and a discriminator, to create and refine synthetic images. In this setup, the generator produces CXR images conditioned on class labels, while the discriminator evaluates the authenticity of these images and predicts their class labels. The generator and discriminator are designed to optimize their performances through a min-max game, iteratively improving the quality of generated images.

The paper utilizes a VGG16-based CNN architecture customized to classify the generated and real CXR images into COVID-positive and normal classes. Fine-tuning of the pretrained VGG16 model allows adaptation to the specific task of COVID-19 detection, with the custom architecture enhancing classification performance.

Numerical Results

The experiments conducted demonstrate that incorporating synthetic images generated by CovidGAN significantly improves the performance of the CNN model. Initially, using only real data, the CNN achieved an accuracy of 85%. With the inclusion of synthetic data, the accuracy increased to 95%, highlighting the efficacy of the proposed augmentation technique. Furthermore, precision and recall metrics for the COVID class improved substantially, suggesting that the synthetic images contribute meaningful information to the model training process.

Implications and Future Directions

The findings of this paper hold significant implications for medical imaging, particularly in scenarios where data scarcity is a major constraint. By effectively augmenting datasets with realistic synthetic images, CovidGAN can enable more robust COVID-19 detection systems, potentially improving early diagnosis and treatment outcomes. The methodological framework presented could be extended to other medical imaging contexts, enhancing the generalization capability of CNNs on limited datasets.

Future research may focus on refining GAN architectures to further enhance image quality and exploring the application of similar techniques to other emergent medical challenges. Training progressive GAN architectures or integrating domain adaptation techniques are potential avenues for improving the performance and reliability of synthetic data augmentation in medical imaging.

This research underscores the potential of GANs in transforming data augmentation strategies, offering a pragmatic solution to data limitations in medical AI applications.