{rfName}
FG

Indexed in

License and Use

Icono OpenAccess

Citations

33

Altmetrics

Analysis of institutional authors

Rashwan HaAuthorAbdulwahab SAuthorAbdel-Nasser MAuthorPuig DAuthor

Share

October 30, 2023
Publications
>
Article
Sí

FGR-Net: Interpretable fundus image gradeability classification based on deep reconstruction learning

Publicated to: Expert Systems With Applications. 238 121644- - 2024-03-15 238(), DOI: 10.1016/j.eswa.2023.121644

Authors:

Khalid, S; Rashwan, HA; Abdulwahab, S; Abdel-Nasser, M; Quiroga, FM; Puig, D
[+]

Affiliations

Aswan Univ, Dept Elect Engn, Aswan 81528, Egypt - Author
Aswan University , Universitat Rovira i Virgili - Author
Facultad de Informatica, Universidad Nacional de La Plata - Author
Rovira & Virgili Univ, Dept Comp Engn & Math, Tarragona, Spain - Author
Univ Al Qadisiyah, Al Diwaniyah 58002, Iraq - Author
Univ Nacl La Plata, Fac Informat, Inst Invest Informat LIDI, La Plata, Buenos Aires, Argentina - Author
Universitat Rovira i Virgili - Author
University of Al-Qadisiyah , Universitat Rovira i Virgili - Author
See more

Abstract

The performance of diagnostic Computer-Aided Design (CAD) systems for retinal diseases depends on the quality of the retinal images being screened. Thus, many studies have been developed to evaluate and assess the quality of such retinal images. However, most of them did not investigate the relationship between the accuracy of the developed models and the quality of the visualization of interpretability methods for distinguishing between gradable and non-gradable retinal images. Consequently, this paper presents a novel framework called “FGR-Net” to automatically assess and interpret underlying fundus image quality by merging an autoencoder network with a classifier network. The FGR-Net model also provides an interpretable quality assessment through visualizations. In particular, FGR-Net uses a deep autoencoder to reconstruct the input image in order to extract the visual characteristics of the input fundus images based on self-supervised learning. The extracted features by the autoencoder are then fed into a deep classifier network to distinguish between gradable and ungradable fundus images. FGR-Net is evaluated with different interpretability methods, which indicates that the autoencoder is a key factor in forcing the classifier to focus on the relevant structures of the fundus images, such as the fovea, optic disk, and prominent blood vessels. Additionally, the interpretability methods can provide visual feedback for ophthalmologists to understand how our model evaluates the quality of fundus images. The experimental results showed the superiority of FGR-Net over the state-of-the-art quality assessment methods, with an accuracy of >89% and an F1-score of >87%. The code is publicly available at https://github.com/saifalkh/FGR-Net.
[+]

Keywords

autoencoder networkdeep learningdiabetic-retinopathyexplainabilitygradabilityintepretabilityocular diseasesquality assessmentsegmentationAutoencoder networkDeep learningExplainabilityGradabilityIntepretabilityNeural-network modelOcular diseasesQuality assessmentRetinal image

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal EXPERT SYSTEMS WITH APPLICATIONS due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2024 there are still no calculated indicators, but in 2023, it was in position 7/106, thus managing to position itself as a Q1 (Primer Cuartil), in the category Operations Research & Management Science. Notably, the journal is positioned above the 90th percentile.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-03:

  • Google Scholar: 8
  • WoS: 9
  • Scopus: 16
[+]

Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-03:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 22 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: http://hdl.handle.net/20.500.11797/imarina9331246
[+]

Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Argentina; Egypt; Iraq.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (Khalid S) and Last Author (Puig Valls, Domènec Savi).

[+]