Detecting Retinal Fundus Image Synthesis by Means of Generative Adversarial Network

Francesco Mercaldo, Francesco Mercaldo, Luca Brunese, Mario Cesarelli, Fabio Martinelli, Antonella Santone

2024

Abstract

The recent introduction of Generative Adversarial Networks has showcased impressive capabilities in producing images that closely resemble genuine ones. As a consequence, concerns have arisen within both the academic and industrial communities regarding the difficulty of distinguishing between counterfeit and authentic images. This matter carries significant importance since images play a crucial role in various fields, such as biomedical image recognition and bioimaging classification. In this paper, we propose a method to discriminate retinal fundus images generated by a Generative Adversarial Network. Following the generation of the bioimages, we employ machine learning to understand whether it is possible to differentiate between real and synthetic retinal fundus images. We consider a Deep Convolutional Generative Adversarial Network, a specific type of Generative Adversarial Network, for retinal fundus image generation. The experimental analysis reveals that even though the generated images are visually indistinguishable from genuine ones, an F-Measure equal to 0.97 is obtained in the discrimination between real and synthetic images. Anyway, this is symptomatic that there are several retinal fundus images that are not classified as such and are thus considered authentic retinal fundus images.

Paper Citation

in Harvard Style

Mercaldo F., Brunese L., Cesarelli M., Martinelli F. and Santone A. (2024). Detecting Retinal Fundus Image Synthesis by Means of Generative Adversarial Network. In Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS; ISBN 978-989-758-688-0, SciTePress, pages 471-478. DOI: 10.5220/0012403100003657

in Bibtex Style

@conference{bioinformatics24,
author={Francesco Mercaldo and Luca Brunese and Mario Cesarelli and Fabio Martinelli and Antonella Santone},
title={Detecting Retinal Fundus Image Synthesis by Means of Generative Adversarial Network},
booktitle={Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS},
year={2024},
pages={471-478},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012403100003657},
isbn={978-989-758-688-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 17th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 1: BIOINFORMATICS
TI - Detecting Retinal Fundus Image Synthesis by Means of Generative Adversarial Network
SN - 978-989-758-688-0
AU - Mercaldo F.
AU - Brunese L.
AU - Cesarelli M.
AU - Martinelli F.
AU - Santone A.
PY - 2024
SP - 471
EP - 478
DO - 10.5220/0012403100003657
PB - SciTePress