# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
###### Abstract  
With the advances in brain imaging, magnetic resonance imaging (MRI) is evolving as a popular radiological tool in clinical diagnosis. Deep learning (DL) methods can detect abnormalities in brain images without an extensive manual feature extraction process. Generative adversarial network (GAN)-synthesized images have many applications in this field besides augmentation, such as image translation, registration, super-resolution, denoising, motion correction, segmentation, reconstruction, and contrast enhancement. The existing literature was reviewed systematically to understand the role of GAN-synthesized dummy images in brain disease diagnosis. Web of Science and Scopus databases were extensively searched to find relevant studies from the last 6 years to write this systematic literature review (SLR). Predefined inclusion and exclusion criteria helped in filtering the search results. Data extraction is based on related research questions (RQ). This SLR identifies various loss functions used in the above applications and software to process brain MRIs. A comparative study of existing evaluation metrics for GAN-synthesized images helps choose the proper metric for an application. GAN-synthesized images will have a crucial role in the clinical sector in the coming years, and this paper gives a baseline for other researchers in the field.  
brain MRI; deep learning; generative adversarial network; conditional GAN; GAN loss function +
Footnote †: journal:  
0000-0001-6697-4800]Y. Y. Y.  
A generative adversarial network (GAN) is a prospective solution to this problem as it can generate training samples duplicating the distribution of the real dataset. In medical imaging, these GAN-synthesized image samples have the forefront use of dataset enhancement; the images can be further used for applications such as image translation, registration, super-resolution, denoising, motion correction, segmentation, reconstruction, and contrast enhancement. GAN was proposed by [PERSON] et al. (2014) in 2014 to generate synthetic images duplicating the ground truth images. A GAN is a combination of two networks, the generator and the discriminator, which are trained at the same time with images. The generator produces fake images duplicating the original images, which are then sent to the discriminator network to be compared and evaluated against the authentic images. The discriminator is trained on both types of inputs and learns the features of the original input. The difference between the two inputs is called discriminator loss, which is conveyed to the generator network to modify the parameters for improvement. The situation is similar to a min-max game where a strategy minimizes the network's maximum possible loss (see Figure 1). This work presents the latest GAN trend for MRI brain image analysis.  
Section 1 of the SLR paper consists of an introduction to the topic. Section 2 presents the methodology, including the review of prior research in the related field, the motivation behind this SLR, research questions, search strategy, and inclusion-exclusion criterion. The results of the SLR guided by research questions are reported in Section 3. The discussion and conclusion are presented in Sections 4 and 5, respectively.