Authors:
Francesco Mercaldo
1
;
2
;
Luca Brunese
2
;
Mario Cesarelli
3
;
Fabio Martinelli
1
and
Antonella Santone
2
Affiliations:
1
Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa, Italy
;
2
Department of Medicine and Health Sciences “Vincenzo Tiberio”, University of Molise, Campobasso, Italy
;
3
Department of Engineering, University of Sannio, Benevento, Italy
Keyword(s):
Retina, GAN, Bioimage, Deep Learning, Classification.
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 gene
rated 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.
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