Performance of GAN-based augmentation for deep learning COVID-19 image classification

Abstract

The biggest challenge in the application of deep learning to the medical domain is the availability of training data. Data augmentation is a typical methodology used in machine learning when confronted with a limited data set. In a classical approach image transformations i.e. rotations, cropping and brightness changes are used. In this work, a StyleGAN2-ADA model of Generative Adversarial Networks is trained on the limited COVID-19 chest X-ray image set. After assessing the quality of generated images they are used to increase the training data set improving its balance between classes. We consider the multi-class classification problem of chest X-ray images including the COVID-19 positive class that hasn't been yet thoroughly explored in the literature. Results of transfer learning-based classification of COVID-19 chest X-ray images are presented. The performance of several deep convolutional neural network models is compared. The impact on the detection performance of classical image augmentations i.e. rotations, cropping, and brightness changes are studied. Furthermore, classical image augmentation is compared with GAN-based augmentation. The most accurate model is an EfficientNet-B0 with an accuracy of 90.2 percent, trained on a dataset with a simple class balancing. The GAN augmentation approach is found to be subpar to classical methods for the considered dataset.

To be published in prceedings of WMLQ2022 International Workshop on Machine Learning and Quantum Computing Applications in Medicine and Physics. Version updated after editorial review

References in corpus (17)

• EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
• GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
• ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
• GAN-based Synthetic Medical Image Augmentation for increased CNN Performance in Liver Lesion Classification
• Can AI help in screening Viral and COVID-19 pneumonia?
• Progressive Growing of GANs for Improved Quality, Stability, and Variation
• Automatic Detection of Coronavirus Disease (COVID-19) Using X-ray Images and Deep Convolutional Neural Networks
• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
• Deep Learning System to Screen Coronavirus Disease 2019 Pneumonia
• Training Generative Adversarial Networks with Limited Data
• Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
• CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection
• Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
• Extracting possibly representative COVID-19 Biomarkers from X-Ray images with Deep Learning approach and image data related to Pulmonary Diseases
• GSA-DenseNet121-COVID-19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID-19 Disease based on Gravitational Search Optimization Algorithm
• Revisiting ResNets: Improved Training and Scaling Strategies
• Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images

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