ArticleEntropy (Basel, Switzerland)2020
Machine Learning Based Automated Segmentation and Hybrid Feature Analysis for Diabetic Retinopathy Classification Using Fundus Image.
Article in Entropy (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Radiomics in ophthalmology: a systematic review.European radiology · 2025Pooled it
- Diagnostics of diabetic retinopathy based on fundus photos using machine learning methods with advanced feature engineering algorithms.Scientific reports · 2025Article
- An intelligent framework for modeling nonlinear irreversible biochemical reactions using artificial neural networks.Scientific reports · 2025Article
- Automatic Detection and Classification of Hypertensive Retinopathy with Improved Convolution Neural Network and Improved SVM.Bioengineering (Basel, Switzerland) · 2024Article
- SwAV-driven diagnostics: new perspectives on grading diabetic retinopathy from retinal photography.Frontiers in robotics and AI · 2024Article
- Wavelet scattering transform application in classification of retinal abnormalities using OCT images.Scientific reports · 2023Article
- A Hybrid Technique for Diabetic Retinopathy Detection Based on Ensemble-Optimized CNN and Texture Features.Diagnostics (Basel, Switzerland) · 2023Article
- Diabetic Retinopathy and Diabetic Macular Edema Detection Using Ensemble Based Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Automated Diabetic Retinopathy Detection Using Horizontal and Vertical Patch Division-Based Pre-Trained DenseNET with Digital Fundus Images.Diagnostics (Basel, Switzerland) · 2022Article
- Study of Rolling Motion of Ships in Random Beam Seas with Nonlinear Restoring Moment and Damping Effects Using Neuroevolutionary Technique.Materials (Basel, Switzerland) · 2022Article
- A Systematic Review of Artificial Intelligence Techniques in Cancer Prediction and Diagnosis.Archives of computational methods in engineering : state of the art reviews · 2022Review
- Application of Intelligent Paradigm through Neural Networks for Numerical Solution of Multiorder Fractional Differential Equations.Computational intelligence and neuroscience · 2022Article
- Mathematical Analysis of Reaction-Diffusion Equations Modeling the Michaelis-Menten Kinetics in a Micro-Disk Biosensor.Molecules (Basel, Switzerland) · 2021Article
- Automated Detection and Diagnosis of Diabetic Retinopathy: A Comprehensive Survey.Journal of imaging · 2021Review
- Precise higher-order reflectivity and morphology models for early diagnosis of diabetic retinopathy using OCT images.Scientific reports · 2021Article
- An Improvised Machine Learning Model Based on Mutual Information Feature Selection Approach for Microbes Classification.Entropy (Basel, Switzerland) · 2021Article
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11 authors.
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No grant is acknowledged in the PubMed record.
Abstract
The object of this study was to demonstrate the ability of machine learning (ML) methods for the segmentation and classification of diabetic retinopathy (DR). Two-dimensional (2D) retinal fundus (RF) images were used. The datasets of DR-that is, the mild, moderate, non-proliferative, proliferative, and normal human eye ones-were acquired from 500 patients at Bahawal Victoria Hospital (BVH), Bahawalpur, Pakistan. Five hundred RF datasets (sized 256 × 256) for each DR stage and a total of 2500 (500 × 5) datasets of the five DR stages were acquired. This research introduces the novel clustering-based automated region growing framework. For texture analysis, four types of features-histogram (H), wavelet (W), co-occurrence matrix (COM) and run-length matrix (RLM)-were extracted, and various ML classifiers were employed, achieving 77.67%, 80%, 89.87%, and 96.33% classification accuracies, respectively. To improve classification accuracy, a fused hybrid-feature dataset was generated by applying the data fusion approach. From each image, 245 pieces of hybrid feature data (H, W, COM, and RLM) were observed, while 13 optimized features were selected after applying four different feature selection techniques, namely Fisher, correlation-based feature selection, mutual information, and probability of error plus average correlation. Five ML classifiers named sequential minimal optimization (SMO), logistic (Lg), multi-layer perceptron (MLP), logistic model tree (LMT), and simple logistic (SLg) were deployed on selected optimized features (using 10-fold cross-validation), and they showed considerably high classification accuracies of 98.53%, 99%, 99.66%, 99.73%, and 99.73%, respectively.
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