ArticleScientific reports2021
Understanding inherent image features in CNN-based assessment of diabetic retinopathy.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed, 65 citations in OpenAlex.
- Current Trends in AI and Eye Disease Diagnostics.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Review
- Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification.Journal of biomedical physics & engineering · 2026Article
- OcuViT: A Vision Transformer-Based Approach for Automated Diabetic Retinopathy and AMD Classification.Journal of imaging informatics in medicine · 2026Article
- A hybrid dual-stream CNN framework with dynamic data augmentation and improved Manta Ray Foraging Optimization for robust glaucoma detection.Scientific reports · 2026Article
- The novel antidiabetic medications on diabetic retinopathy: relevant molecular mechanisms, advancing diagnostic innovations, and therapeutic implications.Frontiers in medicine · 2025Review
- An Artificial Intelligent System for Prostate Cancer Diagnosis in Whole Slide Images.Journal of medical systems · 2024Article
- A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images.Diagnostics (Basel, Switzerland) · 2024Article
- Review
- Automated fingerprint analysis as a diagnostic tool for the genetic disorder Kabuki syndrome.Genetics in medicine open · 2024Article
- A systematic review on diabetic retinopathy detection and classification based on deep learning techniques using fundus images.PeerJ. Computer science · 2024Article
- A fast and fully automated system for glaucoma detection using color fundus photographs.Scientific reports · 2023Article
- Bottom-up and top-down paradigms of artificial intelligence research approaches to healthcare data science using growing real-world big data.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Using Deep Learning Architectures for Detection and Classification of Diabetic Retinopathy.Sensors (Basel, Switzerland) · 2023Article
- A Regression-Based Approach to Diabetic Retinopathy Diagnosis Using Efficientnet.Diagnostics (Basel, Switzerland) · 2023Article
- A Survey on Deep-Learning-Based Diabetic Retinopathy Classification.Diagnostics (Basel, Switzerland) · 2023Review
- EDLDR: An Ensemble Deep Learning Technique for Detection and Classification of Diabetic Retinopathy.Diagnostics (Basel, Switzerland) · 2022Article
- A new detection model of microaneurysms based on improved FC-DenseNet.Scientific reports · 2022Article
- An overview of artificial intelligence in diabetic retinopathy and other ocular diseases.Frontiers in public health · 2022Review
- Evaluation of Structural Retinal Layer Alterations in Retinitis Pigmentosa.Romanian journal of ophthalmologyArticle
Corrections and comments
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Authors and funding
3 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy (DR) is a leading cause of blindness and affects millions of people throughout the world. Early detection and timely checkups are key to reduce the risk of blindness. Automated grading of DR is a cost-effective way to ensure early detection and timely checkups. Deep learning or more specifically convolutional neural network (CNN)-based methods produce state-of-the-art performance in DR detection. Whilst CNN based methods have been proposed, no comparisons have been done between the extracted image features and their clinical relevance. Here we first adopt a CNN visualization strategy to discover the inherent image features involved in the CNN's decision-making process. Then, we critically analyze those features with respect to commonly known pathologies namely microaneurysms, hemorrhages and exudates, and other ocular components. We also critically analyze different CNNs by considering what image features they pick up during learning to predict and justify their clinical relevance. The experiments are executed on publicly available fundus datasets (EyePACS and DIARETDB1) achieving an accuracy of 89 ~ 95% with AUC, sensitivity and specificity of respectively 95 ~ 98%, 74 ~ 86%, and 93 ~ 97%, for disease level grading of DR. Whilst different CNNs produce consistent classification results, the rate of picked-up image features disagreement between models could be as high as 70%.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.