ArticleBioengineering (Basel, Switzerland)2023
Automated Diagnosis of Diabetic Retinopathy Using Deep Learning: On the Search of Segmented Retinal Blood Vessel Images for Better Performance.
Article in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
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Who cites it
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Performance and limitation of machine learning algorithms for diabetic retinopathy screening and its application in health management: a meta-analysis.Biomedical engineering online · 2025Pooled it
- ARTNet: Adaptive channel-wise and Region-aware Transformer Network for diabetic retinopathy segmentation and classification.Frontiers in artificial intelligence · 2026Article
- Optimizing diabetic retinopathy detection with electric fish algorithm and bilinear convolutional networks.Scientific reports · 2025Article
- Harnessing Deep Learning for Accurate Pathological Assessment of Brain Tumor Cell Types.Journal of imaging informatics in medicine · 2025Article
- Attention dual transformer with adaptive temporal convolutional for diabetic retinopathy detection.Scientific reports · 2025Article
- Colour fusion effect on deep learning classification of uveal melanoma.Eye (London, England) · 2024Article
- Tuning attention based long-short term memory neural networks for Parkinson's disease detection using modified metaheuristics.Scientific reports · 2024Article
- Color Fusion Effect on Deep Learning Classification of Uveal Melanoma.Research square · 2023Article
- Discriminative-Region Multi-Label Classification of Ultra-Widefield Fundus Images.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy is one of the most significant retinal diseases that can lead to blindness. As a result, it is critical to receive a prompt diagnosis of the disease. Manual screening can result in misdiagnosis due to human error and limited human capability. In such cases, using a deep learning-based automated diagnosis of the disease could aid in early detection and treatment. In deep learning-based analysis, the original and segmented blood vessels are typically used for diagnosis. However, it is still unclear which approach is superior. In this study, a comparison of two deep learning approaches (Inception v3 and DenseNet-121) was performed on two different datasets of colored images and segmented images. The study's findings revealed that the accuracy for original images on both Inception v3 and DenseNet-121 equaled 0.8 or higher, whereas the segmented retinal blood vessels under both approaches provided an accuracy of just greater than 0.6, demonstrating that the segmented vessels do not add much utility to the deep learning-based analysis. The study's findings show that the original-colored images are more significant in diagnosing retinopathy than the extracted retinal blood vessels.
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