ArticleComputational intelligence and neuroscience2021
Presentation of a Segmentation Method for a Diabetic Retinopathy Patient's Fundus Region Detection Using a Convolutional Neural Network.
Article in Computational intelligence and neuroscience, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 35 citations in OpenAlex.
- AI-assisted Segmentation Tool for Brain Tumor MR Image Analysis.Journal of imaging informatics in medicine · 2025Article
- Point-of-Interest Preference Model Using an Attention Mechanism in a Convolutional Neural Network.Bioengineering (Basel, Switzerland) · 2023Article
- Automatic Detection of Microaneurysms in Fundus Images Using an Ensemble-Based Segmentation Method.Sensors (Basel, Switzerland) · 2023Article
- Three-Dimensional Semantic Segmentation of Diabetic Retinopathy Lesions and Grading Using Transfer Learning.Journal of personalized medicine · 2022Article
- Investigation of Effectiveness of Shuffled Frog-Leaping Optimizer in Training a Convolution Neural Network.Journal of healthcare engineering · 2022Article
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Authors and funding
4 authors at 3 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic retinopathy is characteristic of a local distribution that involves early-stage risk factors and can forecast the evolution of the illness or morphological lesions related to the abnormality of retinal blood flows. Regional variations in retinal blood flow and modulation of retinal capillary width in the macular area and the retinal environment are also linked to the course of diabetic retinopathy. Despite the fact that diabetic retinopathy is frequent nowadays, it is hard to avoid. An ophthalmologist generally determines the seriousness of the retinopathy of the eye by directly examining color photos and evaluating them by visually inspecting the fundus. It is an expensive process because of the vast number of diabetic patients around the globe. We used the IDRiD data set that contains both typical diabetic retinopathic lesions and normal retinal structures. We provided a CNN architecture for the detection of the target region of 80 patients' fundus imagery. Results demonstrate that the approach described here can nearly detect 83.84% of target locations. This result can potentially be utilized to monitor and regulate patients.
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