Diagnosis of diabetic retinopathy using multi level set segmentation algorithm with feature extraction using SVM with selective features

Kandhasamy, J. Pradeep; Balamurali, S.; Kadry, Seifedine; Ramasamy, Lakshmana Kumar

doi:10.1007/s11042-019-7485-8

Cited by 40 publications

(19 citation statements)

References 21 publications

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“…The scheme provides an accuracy of 91 % on the Kaggle dataset. The SVM algorithm [ 16 ] is also used in the diagnosis of diabetic retinopathy that uses a multi-level set segmentation with a genetic algorithm. The features include the texture descriptor estimated by local binary patterns, color moments along with statistical features like mean, median, etc.…”

Section: Related Workmentioning

confidence: 99%

Red lesion in fundus image with hexagonal pattern feature and two-level segmentation

Latha¹,

Bell²,

Sheela³

2022

Multimed Tools Appl

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Red lesion identification at its early stage is very essential for the treatment of diabetic retinopathy to prevent loss of vision. This work proposes a red lesion detection algorithm that uses Hexagonal pattern-based features with two-level segmentation that can detect hemorrhage and microaneurysms in the fundus image. The proposed scheme initially pre-processes the fundus image followed by a two-level segmentation. The level 1 segmentation eliminates the background whereas the level 2 segmentation eliminates the blood vessels that introduce more false positives. A hexagonal pattern-based feature is extracted from the red lesion candidates which can highly differentiate the lesion from non-lesion regions. The hexagonal pattern features are then trained using the recurrent neural network and are classified to eliminate the false negatives. For the evaluation of the proposed red lesion algorithm, the datasets namely ROC challenge, e-ophtha, DiaretDB1, and Messidor are used with the metrics such as Accuracy, Recall, Precision, F1 score, Specificity, and AUC. The scheme provides an average Accuracy, Recall (Sensitivity), Precision, F1 score, Specificity, and AUC of 95.48 % , 84.54 % , 97.3 % , 90.47 % , 86.81 % and 93.43 % respectively.

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Section: Related Workmentioning

confidence: 99%

Red lesion in fundus image with hexagonal pattern feature and two-level segmentation

Latha¹,

Bell²,

Sheela³

2022

Multimed Tools Appl

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“…Clinically, DR is classified in non-proliferative and proliferative DR, and in six stages of retinal degeneration, varying from small bleeding events to significant neovascularization and retinal detachment (Figure 2) [38][39][40]. In addition to structural decline, the stress occurring during DR affects photoreceptor cell function and viability [41].…”

Section: Clinical Management Of Drmentioning

confidence: 99%

Nanomedicine for Treating Diabetic Retinopathy Vascular Degeneration

Bоrodinа

Kostyushev

Zamyatnin

et al. 2021

IJTM

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The incidence of diabetes and the pathological conditions associated with chronic hyperglycemia is increasing worldwide. Among them, diabetic retinopathy represents a leading cause of vision loss, causing a significant structural and functional impairment of the retinal and choroidal capillary network. Current therapies include anti-angiogenic and anti-inflammatory drugs administered through repetitive and invasive intraocular injections, and associated with significant adverse effects. The presence of ocular barriers affects the efficiency of topically administered therapeutics for treating the posterior segment of the eye. In this scenario, nanomedicine could improve current therapies for diabetic retinopathy by providing tools that can decrease the number of injections thanks to their controlled release properties, while some materials showed a natural ability to mitigate pathological neo-angiogenesis. Moreover, specific surface modifications could open new scenarios for the development of topical treatments. This review describes current advances in generating nanomedicine for diabetic retinopathy, focusing on the properties of the different materials tested explicitly for this purpose.

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“…Support vector machines [22] are supervised learning methods with associated learning algorithms. If the vectors are non linearly separable in a space, then the SVM helps to make it linearly separable in a higher-dimensional space.…”

Section: ) Support Vector Machine(svm) Classifiermentioning

confidence: 99%

Automated Binary and Multiclass Classification of Diabetic Retinopathy Using Haralick and Multiresolution Features

et al. 2020

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Diabetic Retinopathy (DR) is considered as the complication of Diabetes Mellitus that damages the blood vessels in the retina. This is characterized as a serious vision-threatening problem in most of the diabetic subjects. Effective automatic classification of diabetic retinopathy is a challenging task in the medical field. The feature extraction plays an eminent role in the effective classification of disease. The proposed work focuses on the extraction of Haralick and Anisotropic Dual-Tree Complex Wavelet Transform (ADTCWT) features that can perform reliable DR classification from retinal fundus images. The Haralick features are based on second-order statistics and ADTCWT reliably extracts the directional features in images. The proposed work concentrates on both binary classification as well as multiclass classification of DR. The system is evaluated across various classifiers such as Support Vector Machine (SVM), Random Forest, Random Tree, J48 classifiers by giving input image features extracted from the MESSIDOR, KAGGLE and DIARETDB0 databases. The performances of the classifiers are analyzed by comparing specificity, precision, recall, False Positive Rate (FPR) and accuracy values for each classifier. The evaluation results show that by applying the proposed feature extraction method, Random Forest outperforms all the other classifiers with an average accuracy of 99.7% and 99.82% for binary and multiclass classification respectively. INDEX TERMS DR binary classification, DR multiclass classification, retinal fundus images, HARALICK, ADTCWT, 10-fold cross validation. S. GAYATHRI received the B.Tech. degree in electronics and communication engineering from the Vivekandha College of Engineering, under Anna university, Chennai, in 2012, and the M.Tech. degree in advanced communication and information systems from the Rajiv Gandhi Institute of Technology, under Mahatma Gandhi University, Kottayam, in 2014. She is currently pursuing the Ph.D. degree in biomedical image processing with the National Institute of Technology at Tiruchirappalli, Tiruchirappalli, India. Her research interests include bio medical signal and image processing, machine learning, and deep learning.

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Diagnosis of diabetic retinopathy using multi level set segmentation algorithm with feature extraction using SVM with selective features

Cited by 40 publications

References 21 publications

Red lesion in fundus image with hexagonal pattern feature and two-level segmentation

Red lesion in fundus image with hexagonal pattern feature and two-level segmentation

Nanomedicine for Treating Diabetic Retinopathy Vascular Degeneration

Automated Binary and Multiclass Classification of Diabetic Retinopathy Using Haralick and Multiresolution Features

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