ArticleInternational journal of ophthalmology2019
Artificial intelligence on diabetic retinopathy diagnosis: an automatic classification method based on grey level co-occurrence matrix and naive Bayesian model.
Article in International journal of ophthalmology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed, 32 citations in OpenAlex.
- Content-based retrieval of fundus images and diabetic retinopathy detection using variants of local texture features.Radiological physics and technology · 2026Article
- A comprehensive review on automated diabetic retinopathy detection and classification using fundus image.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Why aren't we using AI in eye clinics? A systematic review of barriers and solutions in AI-based fundus image diagnostics for ocular diseases.International journal of ophthalmology · 2026Review
- Optimized automated detection of diabetic retinopathy severity: integrating improved multithresholding tunicate swarm algorithm and improved hybrid butterfly optimization.Health information science and systems · 2024Article
- An auxiliary diagnostic tool for common fundus diseases based on fundus color photography and light-weight classification models.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- Automatic Detection and Classification of Diabetic Retinopathy Using the Improved Pooling Function in the Convolution Neural Network.Diagnostics (Basel, Switzerland) · 2023Article
- Deep convolutional neural network based on CT images of pulmonary nodules in the lungs of adolescent and young adult patients with osteosarcoma.Oncology letters · 2023Article
- Vault predicting after implantable collamer lens implantation using random forest network based on different features in ultrasound biomicroscopy images.International journal of ophthalmology · 2023Article
- A Novel Computer-Aided Diagnostic System for Early Detection of Diabetic Retinopathy Using 3D-OCT Higher-Order Spatial Appearance Model.Diagnostics (Basel, Switzerland) · 2022Article
- Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images.Scientific reports · 2021Article
- Visualizing the intellectual structure and recent research trends of diabetic retinopathy.International journal of ophthalmology · 2021Article
- Risk stratification in pulmonary arterial hypertension using Bayesian analysis.The European respiratory journal · 2020Article
- Artificial intelligence: the unstoppable revolution in ophthalmology.Survey of ophthalmologyReview
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo develop an automatic tool on screening diabetic retinopathy (DR) from diabetic patients.
methodsWe extracted textures from eye fundus images of each diabetes subject using grey level co-occurrence matrix method and trained a Bayesian model based on these textures. The receiver operating characteristic (ROC) curve was used to estimate the sensitivity and specificity of the Bayesian model.
resultsA total of 1000 eyes fundus images from diabetic patients in which 298 eyes were diagnosed as DR by two ophthalmologists. The Bayesian model was trained using four extracted textures including contrast, entropy, angular second moment and correlation using a training dataset. The Bayesian model achieved a sensitivity of 0.949 and a specificity of 0.928 in the validation dataset. The area under the ROC curve was 0.938, and the 10-fold cross validation method showed that the average accuracy rate is 93.5%.
conclusionTextures extracted by grey level co-occurrence can be useful information for DR diagnosis, and a trained Bayesian model based on these textures can be an effective tool for DR screening among diabetic patients.
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