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Analysis of institutional authors

Abdulwahab S.AuthorPuig D.AuthorRashwan H.a.Author
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VISTA: vision improvement via split and reconstruct deep neural network for fundus image quality assessment

Publicated to:Neural Computing & Applications. 36 (36): 23149-23168 - 2024-01-01 36(36), DOI: 10.1007/s00521-024-10174-6

Authors: Khalid S; Abdulwahab S; Stanchi OA; Quiroga FM; Ronchetti F; Puig D; Rashwan HA

Affiliations

Facultad de Informática, Universidad Nacional de La Plata; Comision de Investigaciones Cientificas - La Plata - Author
Facultad de Informática, Universidad Nacional de La Plata; Consejo Nacional de Investigaciones Científicas y Técnicas - Author
Universitat Rovira i Virgili - Author
Universitat Rovira i Virgili; University of Al-Qadisiyah - Author

Abstract

Widespread eye conditions such as cataracts, diabetic retinopathy, and glaucoma impact people worldwide. Ophthalmology uses fundus photography for diagnosing these retinal disorders, but fundus images are prone to image quality challenges. Accurate diagnosis hinges on high-quality fundus images. Therefore, there is a need for image quality assessment methods to evaluate fundus images before diagnosis. Consequently, this paper introduces a deep learning model tailored for fundus images that supports large images. Our division method centres on preserving the original image’s high-resolution features while maintaining low computing and high accuracy. The proposed approach encompasses two fundamental components: an autoencoder model for input image reconstruction and image classification to classify the image quality based on the latent features extracted by the autoencoder, all performed at the original image size, without alteration, before reassembly for decoding networks. Through post hoc interpretability methods, we verified that our model focuses on key elements of fundus image quality. Additionally, an intrinsic interpretability module has been designed into the network that allows decomposing class scores into underlying concepts quality such as brightness or presence of anatomical structures. Experimental results in our model with EyeQ, a fundus image dataset with three categories (Good, Usable, and Rejected) demonstrate that our approach produces competitive outcomes compared to other deep learning-based methods with an overall accuracy of 0.9066, a precision of 0.8843, a recall of 0.8905, and an impressive F1-score of 0.8868. The code is publicly available at https://github.com/saifalkhaldiurv/VISTA_-Image-Quality-Assessment.

Keywords
Autoencoder networkExplainabilityFundus imageGradabilityInterpretabilityQuality assessmentRetinal image

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Neural Computing & Applications due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), 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 , thus managing to position itself as a Q1 (Primer Cuartil), in the category Software.

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 2025-05-16:

  • 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: 2 (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.
Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Argentina; 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 (Abdellatif Fatahallah Ibrahim Mahmoud, Hatem).