Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Automatic detection of non-proliferative diabetic retinopathy in retinal fundus images using convolution neural network
P. Saranya, S. Prabakaran
Journal of Ambient Intelligence and Humanized Computing (2020)
Closed Access | Times Cited: 63

Showing 1-25 of 63 citing articles:

A Lightweight Robust Deep Learning Model Gained High Accuracy in Classifying a Wide Range of Diabetic Retinopathy Images
Mohaimenul Azam Khan Raiaan, Kaniz Fatema, Inam Ullah Khan, et al.
IEEE Access (2023) Vol. 11, pp. 42361-42388
Open Access | Times Cited: 57
Improved Support Vector Machine based on CNN-SVD for vision-threatening diabetic retinopathy detection and classification
Anas Bilal, Azhar Imran, Talha Imtiaz Baig, et al.
PLoS ONE (2024) Vol. 19, Iss. 1, pp. e0295951-e0295951
Open Access | Times Cited: 49
A Lesion-Based Diabetic Retinopathy Detection Through Hybrid Deep Learning Model
Ayesha Jabbar, Hannan Bin Liaqat, Aftab Akram, et al.
IEEE Access (2024) Vol. 12, pp. 40019-40036
Open Access | Times Cited: 24
Deep Learning–Based Diabetic Retinopathy Severity Grading System Employing Quadrant Ensemble Model
Charu Bhardwaj, Shruti Jain, Meenakshi Sood
Journal of Digital Imaging (2021) Vol. 34, Iss. 2, pp. 440-457
Open Access | Times Cited: 63
An empirical study of preprocessing techniques with convolutional neural networks for accurate detection of chronic ocular diseases using fundus images
Veena Mayya, S. Sowmya Kamath, Uma Kulkarni, et al.
Applied Intelligence (2022) Vol. 53, Iss. 2, pp. 1548-1566
Open Access | Times Cited: 38
Gray wolf optimization-extreme learning machine approach for diabetic retinopathy detection
Musatafa Abbas Abbood Albadr, Masri Ayob, Sabrina Tiun, et al.
Frontiers in Public Health (2022) Vol. 10
Open Access | Times Cited: 38
EdgeSVDNet: 5G-Enabled Detection and Classification of Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images
Anas Bilal, Xiaowen Liu, Talha Imtiaz Baig, et al.
Electronics (2023) Vol. 12, Iss. 19, pp. 4094-4094
Open Access | Times Cited: 38
Enhancing diabetic retinopathy detection through preprocessing and feature extraction with MGA-CSG algorithm
Ramya Navaneethan, Hemavathi Devarajan
Expert Systems with Applications (2024) Vol. 249, pp. 123418-123418
Closed Access | Times Cited: 9
Disease prediction based retinal segmentation using bi-directional ConvLSTMU-Net
Barkha Rani, Rajeev Ratna Vallabhuni, V. Prasanna Srinivasan, et al.
Journal of Ambient Intelligence and Humanized Computing (2021)
Closed Access | Times Cited: 48
DeepSVDNet: A Deep Learning-Based Approach for Detecting and Classifying Vision-Threatening Diabetic Retinopathy in Retinal Fundus Images
Anas Bilal, Azhar Imran, Talha Imtiaz Baig, et al.
Computer Systems Science and Engineering (2024) Vol. 48, Iss. 2, pp. 511-528
Open Access | Times Cited: 6
Ar-HGSO: Autoregressive-Henry Gas Sailfish Optimization enabled deep learning model for diabetic retinopathy detection and severity level classification
J. Granty Regina Elwin, Jyothi Mandala, Balajee Maram, et al.
Biomedical Signal Processing and Control (2022) Vol. 77, pp. 103712-103712
Closed Access | Times Cited: 26
Exponential gannet firefly optimization algorithm enabled deep learning for diabetic retinopathy detection
Telagarapu Prabhakar, Tianrong Rao, Balajee Maram, et al.
Biomedical Signal Processing and Control (2023) Vol. 87, pp. 105376-105376
Closed Access | Times Cited: 14
Enhanced convolution neural network and improved SVM to detect and classify diabetic retinopathy
Usharani Bhimavarapu
Multimedia Tools and Applications (2024) Vol. 83, Iss. 27, pp. 70321-70342
Closed Access | Times Cited: 5
Deep Transfer Learning-Based Automated Diabetic Retinopathy Detection Using Retinal Fundus Images in Remote Areas
Ayesha Jabbar, Shahid Naseem, Jianqiang Li, et al.
International Journal of Computational Intelligence Systems (2024) Vol. 17, Iss. 1
Open Access | Times Cited: 5
Diabetic Retinopathy Detection using Ensemble Machine Learning
Israa Odeh, Mouhammd Alkasassbeh, Mohammad Alauthman
(2021), pp. 173-178
Closed Access | Times Cited: 29
Quantum chimp-enanced SqueezeNet for precise diabetic retinopathy classification
Anas Bilal, Muhammad Shafiq, Waeal J. Obidallah, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
Open Access
Diabetic retinopathy severity grading employing quadrant‐based Inception‐V3 convolution neural network architecture
Charu Bhardwaj, Shruti Jain, Meenakshi Sood
International Journal of Imaging Systems and Technology (2020) Vol. 31, Iss. 2, pp. 592-608
Closed Access | Times Cited: 29
GO-DBN: Gannet Optimized Deep Belief Network Based wavelet kernel ELM for Detection of Diabetic Retinopathy
S.G. Krishnamoorthy, YU Wei-feng, Jin Luo, et al.
Expert Systems with Applications (2023) Vol. 229, pp. 120408-120408
Closed Access | Times Cited: 9
Multi-classification of eye disease based on fundus images using hybrid Squeeze Net and LRCN model
Meshal Alharbi
Multimedia Tools and Applications (2024) Vol. 83, Iss. 27, pp. 69197-69226
Closed Access | Times Cited: 3
Hybrid hunter-prey ladybug beetle optimization enabled deep learning for diabetic retinopathy classification
Vidya Sagvekar, Manjusha Joshi, Minu Ramakrishnan, et al.
Biomedical Signal Processing and Control (2024) Vol. 95, pp. 106346-106346
Closed Access | Times Cited: 3
Ensemble classification based optimized transfer learning feature method for early stage diagnosis of diabetic retinopathy
Ömer KASIM
Journal of Ambient Intelligence and Humanized Computing (2023) Vol. 14, Iss. 8, pp. 11337-11348
Closed Access | Times Cited: 7
Automated detecting and severity grading of diabetic retinopathy using transfer learning and attention mechanism
Maryam Dinpajhouh, Seyyed Ali Seyyedsalehi
Neural Computing and Applications (2023) Vol. 35, Iss. 33, pp. 23959-23971
Closed Access | Times Cited: 7
Deploying efficient net batch normalizations (BNs) for grading diabetic retinopathy severity levels from fundus images
Summiya Batool, Syed Omer Gilani, Asim Waris, et al.
Scientific Reports (2023) Vol. 13, Iss. 1
Open Access | Times Cited: 6
An IoT based big data framework using equidistant heuristic and duplex deep neural network for diabetic disease prediction
Nithya Rekha Sivakumar, Faten Khalid Karim
Journal of Ambient Intelligence and Humanized Computing (2021)
Closed Access | Times Cited: 14
Light Convolutional Neural Network to Detect Eye Diseases from Retinal Images: Diabetic Retinopathy and Glaucoma
Milon Biswas, Sudipto Chaki, Saurav Mallik, et al.
Lecture notes in networks and systems (2023), pp. 73-83
Closed Access | Times Cited: 5
Page 1 - Next Page

Scroll to top