Title: ReFIGG: retinal fundus image generation using GAN

Authors: Sharika Sasidharan Nair; M.S. Meharban

Addresses: Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kakkanad, Ernakulam, Kerala, India ' Department of Computer Science and Engineering, Rajagiri School of Engineering and Technology, Kakkanad, Ernakulam, Kerala, India

Abstract: The effective training of deep architectures mainly depends on a large number of well-explained data. This is a problem in the medical field where it is hard and costly to gain such images. The tiny blood-vessels of the retina are the only part of the human structure that can be directly and non-intrusively foreseen within the living. Hence, it can be easily obtained and examined by automatic tools. Fundus imaging; a basic check-up process in ophthalmology provides essential data to make it easier for doctors to detect various eye-related diseases at prior phases. Fundus image generation is a challenging process to carry out by constructing composite models of the eye structure. In this paper, we overcome the issue of unavailability of medical fundus datasets by synthesising them artificially through an encoder-decoder generator model to the existing MCML method of generative adversarial networks (GAN) for easier, quicker, and early analysis.

Keywords: fundus image; generative adversarial networks; GAN; encoder-decoder model; image synthesis; deep learning.

DOI: 10.1504/IJCSE.2023.131505

International Journal of Computational Science and Engineering, 2023 Vol.26 No.3, pp.316 - 323

Received: 24 May 2021
Accepted: 27 Mar 2022
Published online: 15 Jun 2023 *

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