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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Systematic Review of Deep Learning Methods Applied to Ocular Images
Oscar Perdómo, Fabio A. González
Ciencia e Ingeniería Neogranadina (2019) Vol. 30, Iss. 1, pp. 9-26
Open Access | Times Cited: 23

Showing 23 citing articles:

Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification
José Camara, Alexandre Neto, Ivan Miguel Pires, et al.
Journal of Imaging (2022) Vol. 8, Iss. 2, pp. 19-19
Open Access | Times Cited: 38
Review of Machine Learning Applications Using Retinal Fundus Images
Yeonwoo Jeong, Yu-Jin Hong, Jae‐Ho Han
Diagnostics (2022) Vol. 12, Iss. 1, pp. 134-134
Open Access | Times Cited: 33
Grading diabetic retinopathy and prostate cancer diagnostic images with deep quantum ordinal regression
Santiago Toledo-Cortés, Diego H. Useche, Henning Müller, et al.
Computers in Biology and Medicine (2022) Vol. 145, pp. 105472-105472
Open Access | Times Cited: 24
Artificial Intelligence in Ophthalmology: Advantages and Limits
Hariton Costin, Monica Fira, Liviu Goraș
Applied Sciences (2025) Vol. 15, Iss. 4, pp. 1913-1913
Open Access
Ocular Disease Recognition using Deep Learning
Kuldeep Vayadande, Varad Ingale, Vivek Verma, et al.
(2022), pp. 1-7
Closed Access | Times Cited: 15
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification
Santiago Toledo-Cortés, Melissa De La Pava, Oscar Perdómo, et al.
Lecture notes in computer science (2020), pp. 206-215
Closed Access | Times Cited: 23
Recognition of Ocular Disease Based Optimized VGG-Net Models
Hanaa Salem, Kareem R. Negm, Mahmoud Y. Shams, et al.
Studies in computational intelligence (2021), pp. 93-111
Closed Access | Times Cited: 19
Feature Extraction Methods for Electroretinogram Signal Analysis: A Review
Soroor Behbahani, Hamid Ahmadieh, Sreeraman Rajan
IEEE Access (2021) Vol. 9, pp. 116879-116897
Open Access | Times Cited: 16
A Conditional Generative Adversarial Network-Based Method for Eye Fundus Image Quality Enhancement
Andrés D. Pérez, Oscar Perdómo, Hernán Andrés Ríos, et al.
Lecture notes in computer science (2020), pp. 185-194
Open Access | Times Cited: 12
SFFT-CapsNet: Stacked Fast Fourier Transform for Retina Optical Coherence Tomography Image Classification using Capsule Network
Michael Opoku, Benjamin Asubam Weyori, Adebayo Felix Adekoya, et al.
International Journal of Advanced Computer Science and Applications (2023) Vol. 14, Iss. 9
Open Access | Times Cited: 4
A transfer learning enabled approach for ocular disease detection and classification
Mahmood ul Hassan, Amin A. Al-Awady, Naeem Ahmed, et al.
Health Information Science and Systems (2024) Vol. 12, Iss. 1
Closed Access | Times Cited: 1
Explainable Artificial Intelligence in Deep Learning Neural Nets-Based Digital Images Analysis
Alexey N. Averkin, Egor N. Volkov, Sergey Yarushev
Journal of Computer and Systems Sciences International (2024) Vol. 63, Iss. 1, pp. 175-203
Closed Access | Times Cited: 1
Explainable artificial intelligence in deep learning neural nets-based digital images analysis
Alexey N. Averkin, Egor N. Volkov, Sergey Yarushev
Известия Российской академии наук Теория и системы управления (2024), Iss. 1, pp. 150-178
Closed Access | Times Cited: 1
Segmentation of retinal fluids and hyperreflective foci using deep learning approach in optical coherence tomography scans
Yeison D. Sanchez, Bernardo Quijano Nieto, Fabio D. Padilla, et al.
(2020)
Closed Access | Times Cited: 7
Special Issue in Artificial Intelligence
Dora María Ballesteros
Ciencia e Ingeniería Neogranadina (2019) Vol. 30, Iss. 1, pp. 7-8
Open Access | Times Cited: 4
Optical Coherence Tomography Image Classification using Light-weight Hybrid Transformers
Disha Singh, Mohammad A. Ammar, Kushagra Varshney, et al.
(2023), pp. 185-189
Closed Access | Times Cited: 1
Deep Learning with a Novel Concoction Loss Function for Identification of Ophthalmic Disease
Sayyid Kamran Hussain, Ali Haider Khan, Malek Alrashidi, et al.
Computers, materials & continua/Computers, materials & continua (Print) (2023) Vol. 76, Iss. 3, pp. 3763-3781
Open Access
An automated retinal hemorrhages and exudates detection in a Mexican image set
Andres Echeveste-Vázquez, Julio C. Armas-Pérez, Arturo González-Vega, et al.
(2023) Vol. 35, pp. 24-24
Closed Access
Deep learning analysis of eye fundus images to support medical diagnosis
Perdomo Charry, Óscar Julián
(2020)
Closed Access
Hybrid Deep Learning Gaussian Process for Diabetic Retinopathy Diagnosis and Uncertainty Quantification
Santiago Toledo-Cortés, Melissa De La Pava, Oscar Perdómo, et al.
arXiv (Cornell University) (2020)
Closed Access
A deep learning model to assess and enhance eye fundus image quality
Pérez Pérez, Andrés Daniel
(2020)
Closed Access
Una revisión de los métodos de Deep Learning aplicados a la detección automatizada de la retinopatía diabética
José Cuello-Navarro, Carlos Barraza Peña, José Escorcia‐Gutierrez
Revista SEXTANTE (2020) Vol. 23, pp. 12-27
Closed Access
What You Need to Know About Artificial Intelligence: Technical Introduction
Oscar Perdómo, Santiago D. Toledo, Alvaro D. Orjuela, et al.
Current practices in ophthalmology (2021), pp. 13-25
Closed Access
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