# A Systematic Literature Review on Applications of GAN-Synthesized Images for Brain MRI
## 3 Results
### Bias-free MRI Scan:
#### 3.1.9 Data Augmentation  
The success of DL models depends on large data samples, which is precisely the constraint of brain MRI. The data augmentation method is used to enlarge the training dataset to improve synthetic image quality without adding new samples to the set. The actions such as translating, rotating, flipping, stretching, and shearing the existing images in the dataset can augment the set; however, these methods lack diversity in the newly generated image samples. The training can become affected towards suboptimal results [143]. Generative modeling maintains similar features to the actual data set while developing the dummy version of the existing images. Deep convolutional GAN generates dummy images using strided convolutions to carry out upsampling in place of max-pooling layers [144]. A timely examination of the brain's status is crucial in preventing [PERSON]'s disease (PD) and hindering its spread. Automatic diagnosis methods use either single-view or multi-view scans to execute the classification or prediction of PD. A WGAN operates on multi-view samples from the MRI dataset containing the cross-sectional view (AXI) and the longitudinal view (SAG). The prodromal class with fewer AXI/SAG MRI data samples causes the problem of over-fitting or under-fitting in an application. Two ResNet networks are trained jointly on the two-view data to create more samples for the prodromal class in AXI and SAG [145]. Class imbalance is a significant issue in abnormal tissue identification and classification in medical analysis. In the case of imbalanced data, a predominant class is filled with usual vital samples, while a subsidiary class is with ailing samples. When a model is trained with a dataset with visible disparity, it generates biased results towards healthy data givingrise to predictable outputs by the network and low sensitivities. The class distribution can be balanced by re-sampling the data space, similarly to oversampling of the predominant class and under-sampling of the subsidiary class, construction of a new compact dataset in an iterative sampling manner by bypassing unessential details, ensemble sampling, and hybrid sampling. A pair-wise GAN architecture uses a cross-modality input to increase heterogeneity in the augmented images. GAN-augmented images are utilized in the pre-training phase, and then real brain MRIs complete advanced training leading to synthetic MR images from one modality to another [146]. Brain tumors are segregated into meningioma, glioma, and pituitary tumors. The direct resemblance in the three classes results in a complex classification procedure in MRI images. A multi-scale gradient GAN (MSG-GAN) synthesizes MRI images with meningioma disease and uses transfer learning to improve classification performance [147]. Noise-to-image and image-to-image GANs enhance the data augmentation (DA) effect. Progressive growing of GAN (PGGAN) is a multistage noise-to-image GAN used for high-resolution MR image generation. Refinement methods such as multimodal unsupervised image-to-image translation (MUNIT) or SimGAN rectify the texture and shape of the images produced by PGGAN close to the originals [148]. A moderate-sized glioma dataset can affect the precise brain tumor categorization using several MRI modalities such as T1-weighted, T1-weighted with contrast-enhanced, T2-weighted, and FLAIR. Pair-wise, GAN trained on two input channels, unlike the normal GAN with only one input channel, augments the brain images to the compact dataset [149]. Two types of perfusion modalities, dynamic susceptibility contrast (DSC) and dynamic contrast-enhanced (DCE), are used to generate realistic relative cerebral blood volume (RCBV). The CGAN is trained on brain tumor perfusion images to learn DSC and DCE parameters with a single gadolinium-based contrast agent administration [150]. AGGrGAN in [151] is a collection of three base GAN models--two variants of deep convolutional GAN (DCGAN) and a WGAN that generate synthetic MRI images of brain tumors.