Artificial MRI Image Generation using Deep Convolutional GAN and its Comparison with other Augmentation Methods
T R Rejusha, Vipin Kumar K. S
Abstract
Deep learning becomes a key technology used in Computer Vision tasks due to its outstanding performances when compared to the traditional methods. It is a machine learning strategy that helps the computer system to learn by itself through examples. But to enhance the training process, deep learning models require rich dataset. In medical imaging applications, it is hard to get such large datasets due to patient privacy laws and the presence of data with incorrect labels. However, data augmentation methods have been adopted to expand the dataset to a large extend. This paper aims to discuss some of the recent approaches of data augmentation that can be used to generate artificial brain MRI images for the detection of Alzheimer’s Disease. To learn the useful aspects of the different augmentation methods, Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset is used. Various data augmentation strategies were employed and their impact on the capabilities of such deep learning methods was studied. Lastly, the most assuring research area of generative adversarial network in data augmentation that generates high-quality synthetic brain MRI images for the diagnosis purpose is identified.