CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection
By Abdul Waheed, Muskan Goyal, Deepak Gupta, Ashish Khanna, F. Al-turjman, P. Pinheiro. IEEE Access, 2020
648 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/4ef0de54bec014b8e2ab9dd48d8b5d961c335a28
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Qiang Yang
- Lawrence Carin
- Jianyi Zhang
- Yu Zhang
- Fatemeh Haghighat
- Weituo Hao
- Gregory P. Spell
- Chunyuan Li
In Radiology, Nuclear Medicine and Imaging
Citations in the corpus
- FLOP: Federated Learning on Medical Datasets using Partial Networks (2021, background): “As communities and organizations across the world continue making efforts to control the pandemic, researchers seek to quicken COVID-19 early detection by automatically classifying computed tomography (CT) scan slices (images) of patients’ chests [7, 20, 29, 30, 40].”
- Pneumonia Classification Using Deep Learning from Chest X-ray Images During COVID-19 (2021, neutral): “AlexNet model is a DL model proposed by Alex Krizhevsky which utilize rectified linear unit (ReLu) in place of Sigmoid function which is used in traditional neural networks [58].”
- Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model (2021, positive): “Since the time series used in this study are also nonlinear and irregular, so it can be said that the results of this study are consistent with previous studies.”