###### Abstract  
Medical image datasets are usually imbalanced due to the high costs of obtaining the data and time-consuming annotations. Training a deep neural network model on such datasets to accurately classify the medical condition does not yield the desired results as they often over-fit the majority class samples' data. Data augmentation is often performed on the training data to address the issue by position augmentation techniques such as scaling, cropping, flipping, padding, rotation, translation, affine transformation, and color augmentation techniques such as brightness, contrast, saturation, and hue to increase the dataset sizes. Radiologists generally use chest X-rays for the diagnosis of pneumonia. Due to patient privacy concerns, access to such data is often protected. In this study, we performed data augmentation on the Chest X-ray dataset to generate artificial chest X-ray images of the under-represented class through generative modeling techniques such as the Deep Convolutional Generative Adversarial Network (DCGAN). With just 1341 chest X-ray images labeled as Normal, artificial samples were created by retaining similar characteristics to the original data with this technique. Evaluating the model resulted in a Frechet Distance of Inception (FID) score of 1.289. We further show the superior performance of a CNN classifier trained on the DCGAN augmented dataset.  
chest X-ray; DCGAN; Frechet Distance of Inception; medical imaging; data augmentation; classification; CNN +
Footnote †: journal:  
utilize two neural networks: a generator network and a discriminator network. Both are deep neural networks. A Generator network takes random noise as input to create samples (data) as realistic as possible to the original dataset, and a discriminator network distinguishes between real (actual data) and data (generated data) data, as shown in Figure 1.  
GANs are a framework that learns to produce realistic objects that are difficult to differentiate from existing real objects, i.e., GANs capture the distribution of training data and generate new examples from the same distribution. GANs are versatile models that consist of two components, typically two distinct neural network models: a generator and a discriminator. The goal of the generator network is to produce plausible/fake examples. In contrast, the discriminator network's goal is to differentiate between the real example from the training data and the fake example generated from the generator network.  
Intuitively, one can think of the generator network as a roger that forges fake examples to try to look as realistic as possible and the discriminator network as an inspector that tries to tell which ones are real and which ones are fake. During training, the generator network gets better and better at producing artificial examples. In contrast, the discriminator learns to become a better investigator and properly distinguish the real and fake samples, i.e., it learns to model the probability of an example being real or fake. The probability of an example being real or fake from the discriminator is the one that helps the generator network to produce better examples/samples over time. This game's equilibrium is where the generator produces realistic fake examples that look similar to actual examples from the training data. Simultaneously, the discriminator is left guessing at a 50% probability that the generated example is real or fake.