| dc.description.abstract | Brain stroke is seen as a very vital problem due to its possible health consequences
and incidence. It is the second cause of premature death worldwide. Systems that
can assist the decision process of doctors are of vital importance, as they need intervention
as soon as possible after a possible brain stroke.
Studies on detecting brain stroke in the fields of image processing and machine
learning often encounter the problem of not having enough labeled data. This has a
significant impact on the success percentages of the studies carried out.
Based on this problem, this thesis aims to synthetically generate Perfusion CT images
of patients affected by brain stroke with the Generative adversarial networks
(GANs). In order to do that, different architectures such as Deep Convolutional Generative
Adversarial Network (DCGAN), Wasserstein Generative Adversarial Network
(WGAN), and Motion and Content Decomposed Generative Adversarial Network
(mocoGAN) are investigated and modified for this problem and as a result,
Perfusion CT images are generated. | |
