Synthetic Brain Images: Bridging the Gap in Brain Mapping With
  Generative Adversarial Model
Drici Mourad Kazeem Oluwakemi Oseni
Magnetic Resonance Imaging (MRI) is a vital modality for gaining precise
anatomical information, and it plays a significant role in medical imaging for
diagnosis and therapy planning. Image synthesis problems have seen a revolution
in recent years due to the introduction of deep learning techniques,
specifically Generative Adversarial Networks (GANs). This work investigates the
use of Deep Convolutional Generative Adversarial Networks (DCGAN) for producing
high-fidelity and realistic MRI image slices. The suggested approach uses a
dataset with a variety of brain MRI scans to train a DCGAN architecture. While
the discriminator network discerns between created and real slices, the
generator network learns to synthesise realistic MRI image slices. The
generator refines its capacity to generate slices that closely mimic real MRI
data through an adversarial training approach. The outcomes demonstrate that
the DCGAN promise for a range of uses in medical imaging research, since they
show that it can effectively produce MRI image slices if we train them for a
consequent number of epochs. This work adds to the expanding corpus of research
on the application of deep learning techniques for medical image synthesis. The
slices that are could be produced possess the capability to enhance datasets,
provide data augmentation in the training of deep learning models, as well as a
number of functions are made available to make MRI data cleaning easier, and a
three ready to use and clean dataset on the major anatomical plans.