ArticleJournal of medical systems2019
A Novel Approach for Detection of Hard Exudates Using Random Forest Classifier.
Article in Journal of medical systems, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- MAFNet: A novel adaptive multi-scale model for fine-grained grading of diabetic retinopathy.Scientific reports · 2025Article
- Machine learning model-based prediction of postpancreatectomy acute pancreatitis following pancreaticoduodenectomy: A retrospective cohort study.World journal of gastroenterology · 2025Article
- Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.BMC medical informatics and decision making · 2024Article
- Deep attentive convolutional neural network for automatic grading of imbalanced diabetic retinopathy in retinal fundus images.Biomedical optics express · 2022Article
- Deep CNN with Hybrid Binary Local Search and Particle Swarm Optimizer for Exudates Classification from Fundus Images.Journal of digital imaging · 2022Article
- Machine Learning-Based Shear Wave Elastography Elastic Index (SWEEI) in Predicting Cervical Lymph Node Metastasis of Papillary Thyroid Microcarcinoma: A Comparative Analysis of Five Practical Prediction Models.Cancer management and research · 2022Article
- Predictive models for small-for-gestational-age births in women exposed to pesticides before pregnancy based on multiple machine learning algorithms.Frontiers in public health · 2022Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Diabetic Retinopathy is the major cause of blindness for diabetics in which the retina is damaged. Regular screening system help in detecting the early symptoms like exudates, which are due to the leakage of blood pressure of vessels. The significant role of proposed system is detecting the hard exudates in prevention of visual loss and blindness. Many researchers studied and investigated about detecting the exudates region but not satisfied with their results. Fundamental medical image processing steps with different techniques are implemented by the proposed system. Random Forest is a novel classification which is applied on color retinal images able to classify cluster of data with high accuracy. The performance of the proposed system is obtained by analyzing the accuracy obtained from the Random Forest classifier. These images are obtained from Diabetic Retinopathy Database (DIARETDB) database. The simulation results are obtained with the help of MATLAB 2018. By applying novel classification techniques improves the automatic detection of hard exudates from color retinal images. The achieved accuracy is compared with existing classifiers since the proposed Random Forest classifier provides the accuracy of 99.89% applied on color retinal images.
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