Medical MR Image Synthesis using DCGAN

Generative Adversarial Networks (GANs) have been extensively gained considerable attention since 2014. Irrefutably saying, their most remarkable success has been made in domains such as computer vision and medical image processing. Despite the noteworthy success attained to date, applying GANs to re...

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Bibliographic Details
Published in2022 First International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) pp. 01 - 04
Main Authors Divya, S, Suresh, L Padma, John, Ansamma
Format Conference Proceeding
LanguageEnglish
Published IEEE 16.02.2022
Subjects
brain tumor
Computational modeling
Computer architecture
Computer vision
Focusing
GAN
Generative adversarial networks
Image synthesis
Magnetic resonance imaging
medical image processing
MRI
synthetic data
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Summary:Generative Adversarial Networks (GANs) have been extensively gained considerable attention since 2014. Irrefutably saying, their most remarkable success has been made in domains such as computer vision and medical image processing. Despite the noteworthy success attained to date, applying GANs to real world problems still posses significant challenges, one among which is diversity of image generation and detection of fake images from real ones. Focusing on the extend to which various GAN models have made headway against these challenges, this study provides an overview of DCGAN architecture and its application as a synthetic data generator and act an a binary classifier, which detects real or fake images using brain tumorous Magnetic Resonance Imaging (MRI) dataset.
DOI:10.1109/ICEEICT53079.2022.9768647