Proceedings ArticleDOI
Diagnosis of diabetic retinopathy using morphological process and SVM classifier
Mahendran Gandhi,R. Dhanasekaran +1 more
- pp 873-877
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TLDR
This paper focuses on automatic detection of diabetic retinopathy through detecting exudates in colour fundus retinal images and also classifies the rigorousness of the lesions.Abstract:
Diabetic Retinopathy (DR), the most common eye disease of the diabetic patients, occurs when small blood vessels gets damaged in the retina, due to high glucose level. It affects 80% of all patients who have had diabetes for 10 years or more, which can also lead to vision loss. Detection of diabetic retinopathy in advance, protects patients from vision loss. The major symptom of diabetic retinopathy is the exudates. Exudate is a fluid that filters from the circulatory system into lesions or area of inflammation. Detecting retinal fundus diseases in an early stage, helps the ophthalmologists apply proper treatments that might eliminate the disease or decrease the severity of it. This paper focuses on automatic detection of diabetic retinopathy through detecting exudates in colour fundus retinal images and also classifies the rigorousness of the lesions. Decision making of the severity level of the disease was performed by SVM classifier.read more
Citations
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Journal ArticleDOI
Deep-learning-based automatic computer-aided diagnosis system for diabetic retinopathy
TL;DR: The proposed AlexNet DNN-based DR exhibits a better performance with LDA feature selection, where it exhibits a DR classification accuracy of 97.93% with FC7 features, whereas with PCA, it shows 95.26% accuracy.
Proceedings ArticleDOI
Automated detection of diabetic retinopathy using SVM
TL;DR: A computer-assisted diagnosis based on the digital processing of retinal images to automatically classify the grade of non-proliferative diabetic retinopathy at any retinal image is proposed.
Proceedings ArticleDOI
An Interpretable Ensemble Deep Learning Model for Diabetic Retinopathy Disease Classification
TL;DR: An automatic image-level DR detection system using multiple well-trained deep learning models using the Adaboost algorithm and weighted class activation maps (CAMs) that can illustrate the suspected position of lesions are presented.
Proceedings ArticleDOI
Diagnosis of diabetic retinopathy using machine learning classification algorithm
TL;DR: This review paper focuses on decision about the presence of disease by applying ensemble of machine learning classifying algorithms on features extracted from output of different retinal image processing algorithms, like diameter of optic disk, lesion specific, image level, and so on.
Proceedings ArticleDOI
Diabetic Retinopathy using morphological operations and machine learning
TL;DR: The main objective of this proposed work is to detect retinal micro-aneurysms and exudates for automatic screening of DR using Support Vector Machine (SVM) and KNN classifier.
References
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Journal ArticleDOI
Automatic detection of diabetic retinopathy exudates from non-dilated retinal images using mathematical morphology methods
TL;DR: This paper investigates and proposes a set of optimally adjusted morphological operators to be used for exudate detection on diabetic retinopathy patients' non-dilated pupil and low-contrast images and results are successful.
Journal ArticleDOI
An Ensemble-Based System for Microaneurysm Detection and Diabetic Retinopathy Grading
Balint Antal,Andras Hajdu +1 more
TL;DR: This work proposes an ensemble-based framework to improve microaneurysm detection by considering the output of multiple classifiers and a combination of internal components of microaneuysm detectors, namely preprocessing methods and candidate extractors.
Journal ArticleDOI
Multiscale AM-FM Methods for Diabetic Retinopathy Lesion Detection
Carla Agurto,Victor Murray,E. S. Barriga,S. Murillo,Marios S. Pattichis,Herbert Davis,Stephen R. Russell,Michael D. Abràmoff,Peter Soliz +8 more
TL;DR: The proposed AM-FM methodology shows significant capability for use in automatic DR screening and is demonstrated by applying it to classification of retinal images from the MESSIDOR database.
Journal ArticleDOI
A Computational-Intelligence-Based Approach for Detection of Exudates in Diabetic Retinopathy Images
TL;DR: The proposed scheme illustrated an accuracy including 93.5% sensitivity and 92.1% predictivity for identification of retinal exudates at the pixel level for automated identification of exudate pathologies in retinopathy images based on computational intelligence techniques.
Proceedings ArticleDOI
Automated feature extraction for early detection of diabetic retinopathy in fundus images
TL;DR: A new constraint for optic disk detection is proposed where the major blood vessels are first detected and the intersection of these are used to find the approximate location of the optic disk.