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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:

Detection of diabetic retinopathy using a fusion of textural and ridgelet features of retinal images and sequential minimal optimization classifier
Lakshmana Kumar Ramasamy, Shynu Gopalan Padinjappurathu, Seifedine Kadry, et al.
PeerJ Computer Science (2021) Vol. 7, pp. e456-e456
Open Access | Times Cited: 61

Showing 1-25 of 61 citing articles:

Applying supervised contrastive learning for the detection of diabetic retinopathy and its severity levels from fundus images
Md. Robiul Islam, Lway Faisal Abdulrazak, Md. Nahiduzzaman, et al.
Computers in Biology and Medicine (2022) Vol. 146, pp. 105602-105602
Open Access | Times Cited: 88
A deep neural network and machine learning approach for retinal fundus image classification
Rohit M. Thanki
Healthcare Analytics (2023) Vol. 3, pp. 100140-100140
Open Access | Times Cited: 47
Effective Handling of Missing Values in Datasets for Classification Using Machine Learning Methods
Ashokkumar Palanivinayagam, Robertas Damaševičius
Information (2023) Vol. 14, Iss. 2, pp. 92-92
Open Access | Times Cited: 41
Adversarial Attack and Defence through Adversarial Training and Feature Fusion for Diabetic Retinopathy Recognition
Sheeba Lal, Saeed Ur Rehman, Jamal Hussain Shah, et al.
Sensors (2021) Vol. 21, Iss. 11, pp. 3922-3922
Open Access | Times Cited: 100
COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion
Muhammad Attique Khan, Majed Alhaisoni, Usman Tariq, et al.
Sensors (2021) Vol. 21, Iss. 21, pp. 7286-7286
Open Access | Times Cited: 78
Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients
Sarmad Maqsood, Robertas Damaševičius, Rytis Maskeliūnas
Sensors (2021) Vol. 21, Iss. 11, pp. 3865-3865
Open Access | Times Cited: 62
Classification of diabetic retinopathy with feature selection over deep features using nature-inspired wrapper methods
Murat Canayaz
Applied Soft Computing (2022) Vol. 128, pp. 109462-109462
Closed Access | Times Cited: 56
Classification and Segmentation of Diabetic Retinopathy: A Systemic Review
Natasha Shaukat, Javeria Amin, Muhammad Imran Sharif, et al.
Applied Sciences (2023) Vol. 13, Iss. 5, pp. 3108-3108
Open Access | Times Cited: 21
Dual branch deep learning network for detection and stage grading of diabetic retinopathy
Hossein Shakibania, Sina Raoufi, Behnam Pourafkham, et al.
Biomedical Signal Processing and Control (2024) Vol. 93, pp. 106168-106168
Open Access | Times Cited: 8
Retinal Disease Diagnosis Using Deep Learning on Ultra-Wide-Field Fundus Images
Toan Duc Nguyen, Duc-Tai Le, Junghyun Bum, et al.
Diagnostics (2024) Vol. 14, Iss. 1, pp. 105-105
Open Access | Times Cited: 7
Diabetic retinopathy prediction based on deep learning and deformable registration
Mohammed Oulhadj, Jamal Riffi, Chaimae Khodriss, et al.
Multimedia Tools and Applications (2022) Vol. 81, Iss. 20, pp. 28709-28727
Closed Access | Times Cited: 34
Lung X-Ray Image Segmentation Using Heuristic Red Fox Optimization Algorithm
Antoni Jaszcz, Dawid Połap, Robertas Damaševičius
Scientific Programming (2022) Vol. 2022, pp. 1-8
Open Access | Times Cited: 34
Diabetic retinopathy detection using ensembled transfer learning based thrice CNN with SVM classifier
Neetha Merin Thomas, S. Albert Jerome
Multimedia Tools and Applications (2024) Vol. 83, Iss. 27, pp. 70089-70115
Closed Access | Times Cited: 6
Edge-aware Feature Aggregation Network for Polyp Segmentation
Tao Zhou, Yizhe Zhang, Geng Chen, et al.
Deleted Journal (2025) Vol. 22, Iss. 1, pp. 101-116
Closed Access
Deep learning enabled optimized feature selection and classification for grading diabetic retinopathy severity in the fundus image
A. Mary Dayana, W. R. Sam Emmanuel
Neural Computing and Applications (2022) Vol. 34, Iss. 21, pp. 18663-18683
Closed Access | Times Cited: 25
A study on effective data preprocessing and augmentation method in diabetic retinopathy classification using pre-trained deep learning approaches
Ramazan İncir, Ferhat Bozkurt
Multimedia Tools and Applications (2023) Vol. 83, Iss. 4, pp. 12185-12208
Closed Access | Times Cited: 14
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: 4
Classification of Diabetic Retinopathy Severity Based on GCA Attention Mechanism
Binhua Yang, Tongyan Li, Haidi Xie, et al.
IEEE Access (2021) Vol. 10, pp. 2729-2739
Open Access | Times Cited: 31
Prediction of Diabetic Retinopathy using Deep Learning with Preprocessing
S. Balaji, B. Karthik, D Gokulakrishnan
EAI Endorsed Transactions on Pervasive Health and Technology (2024) Vol. 10
Open Access | Times Cited: 3
Lesion-aware attention with neural support vector machine for retinopathy diagnosis
Nagur Shareef Shaik, Teja Krishna Cherukuri
Machine Vision and Applications (2021) Vol. 32, Iss. 6
Closed Access | Times Cited: 25
Multi-Scale Feature Fusion with Adaptive Weighting for Diabetic Retinopathy Severity Classification
Runze Fan, Yuhong Liu, Rongfen Zhang
Electronics (2021) Vol. 10, Iss. 12, pp. 1369-1369
Open Access | Times Cited: 23
A Comprehensive Review of Diabetic Retinopathy Detection and Grading Based on Deep Learning and Metaheuristic Optimization Techniques
A. Mary Dayana, W. R. Sam Emmanuel
Archives of Computational Methods in Engineering (2023) Vol. 30, Iss. 7, pp. 4565-4599
Closed Access | Times Cited: 9
Detection of retinopathy disease using morphological gradient and segmentation approaches in fundus images
Mesut Toğaçar
Computer Methods and Programs in Biomedicine (2021) Vol. 214, pp. 106579-106579
Closed Access | Times Cited: 21
Optimized quaternion radial Hahn Moments application to deep learning for the classification of diabetic retinopathy
Mohamed Amine Tahiri, Hicham Amakdouf, Mostafa El Mallahi, et al.
Multimedia Tools and Applications (2023) Vol. 82, Iss. 30, pp. 46217-46240
Closed Access | Times Cited: 8
Grading the severity of diabetic retinopathy using an ensemble of self-supervised pre-trained convolutional neural networks: ESSP-CNNs
Saeed Parsa, Toktam Khatibi
Multimedia Tools and Applications (2024)
Closed Access | Times Cited: 2
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