The University of Southampton
University of Southampton Institutional Repository

Medical MR image synthesis using DCGAN

Medical MR image synthesis using DCGAN
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 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
brain tumor, GAN, medical image processing, MRI, synthetic data
IEEE
Divya, S.
3ee4e63f-4f55-41da-80ae-18de34842645
Suresh, L. Padma
889c4801-773e-4d99-9638-f28436a99906
John, Ansamma
90d64164-ef20-4bc3-9f84-fd5d513a15b0
Divya, S.
3ee4e63f-4f55-41da-80ae-18de34842645
Suresh, L. Padma
889c4801-773e-4d99-9638-f28436a99906
John, Ansamma
90d64164-ef20-4bc3-9f84-fd5d513a15b0

Divya, S., Suresh, L. Padma and John, Ansamma (2022) Medical MR image synthesis using DCGAN. In 2022 1st International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2022. IEEE.. (doi:10.1109/ICEEICT53079.2022.9768647).

Record type: Conference or Workshop Item (Paper)

Abstract

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

Text
Medical_MR_Image_Synthesis_using_DCGAN_IEEE
Restricted to Repository staff only
Available under License Other.
Request a copy

More information

Published date: 2022
Venue - Dates: 1st International Conference on Electrical, Electronics, Information and Communication Technologies, ICEEICT 2022, , Trichy, India, 2022-02-16 - 2022-02-18
Keywords: brain tumor, GAN, medical image processing, MRI, synthetic data

Identifiers

Local EPrints ID: 501670
URI: http://eprints.soton.ac.uk/id/eprint/501670
PURE UUID: 03c31b65-8d26-4b57-8188-9b68bfe08a5f
ORCID for S. Divya: ORCID iD orcid.org/0000-0002-7302-7146

Catalogue record

Date deposited: 05 Jun 2025 16:47
Last modified: 06 Jun 2025 02:13

Export record

Altmetrics

Contributors

Author: S. Divya ORCID iD
Author: L. Padma Suresh
Author: Ansamma John

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×