Data Augmentation vs. Synthetic Data Generation: An Empirical Evaluation for Enhancing Radiology Image Classification

Radiology is a field of medicine dealing with diagnostic images to detect diseases for further treatment. Collecting and annotating diagnostic images like Magnetic Resonance Imaging (MRI) and X-Ray is a rigorous and time-consuming process. Deep Learning methods are widely utilized for disease classification and prediction from diagnostic images, but they demand substantial amounts of training data. Additionally, certain diseases are uncommon in large patient cohorts, posing difficulties in obtaining sufficient imaging samples to construct accurate deep learning models. Data augmentation techniques are commonly used to overcome this challenge of limited data. These techniques involve applying geometric transformations such as rotation, cropping, zooming, flipping, and other similar operations to the images to enlarge the dataset artificially. Another possible way of expanding the dataset is by synthesizing data to generate artificial medical images by mimicking the original images. This study presents RAD-DCGAN: A Deep Convolutional Generative Adversarial Network to produce high-resolution synthetic radiology images from the X-ray and MRI images collected from a private medical hospital (KMC Hospital, India). We aim to determine the most effective technique for enhancing the performance of radiology image classifiers by comparing and evaluating the proposed RAD-DCGAN with the standard data augmentation strategy. Our empirical evaluation, which involved eight standard deep learning models, demonstrated that deep learning classifiers trained on synthetic radiology data outperformed those trained on standard augmented data. The utilization of the RAD-DCGAN model for training and testing deep learning models on synthetic data has demonstrated a notable improvement of 4-5% in accuracy compared to conventional augmentation techniques. This signifies the state-of-the-art performance achieved by the RAD-DCGAN model in enhancing the accuracy of deep learning models.