Synthetic Brain Images: Bridging the Gap in Brain Mapping With Generative Adversarial Model

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Synthetic Brain Images: Bridging the Gap in Brain Mapping With Generative Adversarial Model

Authors

Drici Mourad1and Dr. Kazeem Oluwakemi Oseni2

1School of Computer Science and Engineering, University of Westminster, London,

United Kingdom

2School of Computer Science and Technology, University of Bedfordshire, United

Kingdom

Abstract  

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.

Keywords

Magnetic Resonance Imaging, Generative Adversarial Network, Deep Convolutional Generative Adversarial Network, Nifty, OpenNeuro

Paper URL

https://aircconline.com/ijmit/V16N1/16124ijmit01.pdf

Volume URL:

https://airccse.org/journal/ijmit/vol16.html

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International Journal of Managing Information Technology (IJMIT) -h-index 29

(WJCI Indexed )

https://airccse.org/journal/ijmit/ijmit.html

WJCI Impact Factor : 0.040

ISSN: 0975-5586 (Online); 0975-5926 (Print)

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