Additional Look into GAN-based Augmentation for Deep Learning COVID-19
Image Classification
- URL: http://arxiv.org/abs/2401.14705v2
- Date: Fri, 2 Feb 2024 20:53:01 GMT
- Title: Additional Look into GAN-based Augmentation for Deep Learning COVID-19
Image Classification
- Authors: Oleksandr Fedoruk, Konrad Klimaszewski, Aleksander Ogonowski and
Micha{\l} Kruk
- Abstract summary: We study the dependence of the GAN-based augmentation performance on dataset size with a focus on small samples.
We train StyleGAN2-ADA with both sets and then, after validating the quality of generated images, we use trained GANs as one of the augmentations approaches in multi-class classification problems.
The GAN-based augmentation approach is found to be comparable with classical augmentation in the case of medium and large datasets but underperforms in the case of smaller datasets.
- Score: 57.1795052451257
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The availability of training data is one of the main limitations in deep
learning applications for medical imaging. Data augmentation is a popular
approach to overcome this problem. A new approach is a Machine Learning based
augmentation, in particular usage of Generative Adversarial Networks (GAN). In
this case, GANs generate images similar to the original dataset so that the
overall training data amount is bigger, which leads to better performance of
trained networks. A GAN model consists of two networks, a generator and a
discriminator interconnected in a feedback loop which creates a competitive
environment. This work is a continuation of the previous research where we
trained StyleGAN2-ADA by Nvidia on the limited COVID-19 chest X-ray image
dataset. In this paper, we study the dependence of the GAN-based augmentation
performance on dataset size with a focus on small samples. Two datasets are
considered, one with 1000 images per class (4000 images in total) and the
second with 500 images per class (2000 images in total). We train StyleGAN2-ADA
with both sets and then, after validating the quality of generated images, we
use trained GANs as one of the augmentations approaches in multi-class
classification problems. We compare the quality of the GAN-based augmentation
approach to two different approaches (classical augmentation and no
augmentation at all) by employing transfer learning-based classification of
COVID-19 chest X-ray images. The results are quantified using different
classification quality metrics and compared to the results from the literature.
The GAN-based augmentation approach is found to be comparable with classical
augmentation in the case of medium and large datasets but underperforms in the
case of smaller datasets. The correlation between the size of the original
dataset and the quality of classification is visible independently from the
augmentation approach.
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