ArticleSensors (Basel, Switzerland)2021
An Automatic Detection and Classification System of Five Stages for Hypertensive Retinopathy Using Semantic and Instance Segmentation in DenseNet Architecture.
Article in Sensors (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- The Underutilized Ocular Fundus in Emergency Departments: Current Progress and Future Prospect of Imaging and Artificial Intelligence for Patient Care.Ophthalmology science · 2026Review
- Employing machine learning techniques for non-invasive blood pressure classification using photoplethysmography signals.Physical and engineering sciences in medicine · 2026Article
- Artificial Intelligence (AI) for Early Diagnosis of Retinal Diseases.Medicina (Kaunas, Lithuania) · 2024Review
- Color-Transfer-Enhanced Data Construction and Validation for Deep Learning-Based Upper Gastrointestinal Landmark Classification in Wireless Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2024Article
- Arteriovenous Length Ratio: A Novel Method for Evaluating Retinal Vasculature Morphology and Its Diagnostic Potential in Eye-Related Diseases.Journal of imaging · 2023Article
- Mobile-HR: An Ophthalmologic-Based Classification System for Diagnosis of Hypertensive Retinopathy Using Optimized MobileNet Architecture.Diagnostics (Basel, Switzerland) · 2023Article
- Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.Frontiers in cell and developmental biology · 2023Review
- Classification of Blood Pressure Levels Based on Photoplethysmogram and Electrocardiogram Signals with a Concatenated Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Research Progress of Artificial Intelligence Image Analysis in Systemic Disease-Related Ophthalmopathy.Disease markers · 2022Review
- Automated Diagnosis of Hypertensive Retinopathy Using Res-UNet and Graph Convolutional Networks.Healthcare technology lettersArticle
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Authors and funding
3 authors.
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Abstract
The stage and duration of hypertension are connected to the occurrence of Hypertensive Retinopathy (HR) of eye disease. Currently, a few computerized systems have been developed to recognize HR by using only two stages. It is difficult to define specialized features to recognize five grades of HR. In addition, deep features have been used in the past, but the classification accuracy is not up-to-the-mark. In this research, a new hypertensive retinopathy (HYPER-RETINO) framework is developed to grade the HR based on five grades. The HYPER-RETINO system is implemented based on pre-trained HR-related lesions. To develop this HYPER-RETINO system, several steps are implemented such as a preprocessing, the detection of HR-related lesions by semantic and instance-based segmentation and a DenseNet architecture to classify the stages of HR. Overall, the HYPER-RETINO system determined the local regions within input retinal fundus images to recognize five grades of HR. On average, a 10-fold cross-validation test obtained sensitivity (SE) of 90.5%, specificity (SP) of 91.5%, accuracy (ACC) of 92.6%, precision (PR) of 91.7%, Matthews correlation coefficient (MCC) of 61%, F1-score of 92% and area-under-the-curve (AUC) of 0.915 on 1400 HR images. Thus, the applicability of the HYPER-RETINO method to reliably diagnose stages of HR is verified by experimental findings.
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