Automatic Detection of Diabetic Retinopathy Using Support Vector Machine

Excess amount of insulin in human blood might affect the retina in eyes and cause abnormalities in human vision, which is generally termed as Diabetic Retinopathy (DR). Many diabetic patients are often saved by the earlier diagnosis of Diabetic Retinopathy. The surface of retinal layer that has the earlier signs of Diabetic Retinopathy. This type of abnormalities are detected using traditional image processing methods which includes stages such as capturing fundus images, preprocessing, feature extraction and finally classification is performed to classify it as retinal and healthy images. (The proposed system, this detection is completed by Fuzzy-C Means (FCM) clustering). The proposed automated system consists of four phases which includes, preprocessing of the captured fundus images in which the image is resized and the second stage involves CLAHE. Images has to enhanced in order to boost up the features for which Contrast adjustment is performed in the third phase and before classification the grey and green channels of the images are extracted from the processed images. This detection process provides better results than the prevailing method. SVM classifier has been used in the proposed framework which classified the malady level of diabetic retinopathy in eye. The proposed system manages to provide better classification rates compared to the previous methodologies. The accuracy, sensitivity and specificity of the developed automated system was found to be 94.4%, 100% and 85.7%, which was promising than the compared methods.

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An automated grading system for diabetic retinopathy using curvelet transform and hierarchical classification

International Journal of Engineering & Technology ◽

10.14419/ijet.v7i2.15.11375 ◽

2018 ◽

Vol 7 (2.15) ◽

pp. 154 ◽

Cited By ~ 1

Author(s):

Fanji Ari Mukti ◽

C Eswaran ◽

Noramiza Hashim ◽

Ho Chiung Ching ◽

Mohamed Uvaze Ahamed Ayoobkhan

Keyword(s):

Diabetic Retinopathy ◽

Hierarchical Classification ◽

Curvelet Transform ◽

Automated System ◽

Support Vector ◽

Svm Classifier ◽

Fundus Images ◽

Hard Exudates ◽

Automated Grading ◽

Fast Discrete Curvelet Transform

In this paper, an automated system for grading the severity level of Diabetic Retinopathy (DR) disease based on fundus images is presented. Features are extracted using fast discrete curvelet transform. These features are applied to hierarchical support vector machine (SVM) classifier to obtain four types of grading levels, namely, normal, mild, moderate and severe. These grading levels are determined based on the number of anomalies such as microaneurysms, hard exudates and haemorrhages that are present in the fundus image. The performance of the proposed system is evaluated using fundus images from the Messidor database. Experiment results show that the proposed system can achieve an accuracy rate of 86.23%.

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An efficient automated system for detection of diabetic retinopathy from fundus images using support vector machine and bayesian classifiers

2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET) ◽

10.1109/icceet.2012.6203804 ◽

2012 ◽

Cited By ~ 15

Author(s):

K. Narasimhan ◽

V.C. Neha ◽

K. Vijayarekha

Keyword(s):

Support Vector Machine ◽

Diabetic Retinopathy ◽

Automated System ◽

Support Vector ◽

Fundus Images ◽

Bayesian Classifiers

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Automatic detection and classification of diabetic retinopathy from retinal fundus images

10.32920/ryerson.14648760 ◽

2021 ◽

Author(s):

Abdullah Biran

Keyword(s):

Diabetic Retinopathy ◽

Gabor Filter ◽

Automatic Detection ◽

Histogram Equalization ◽

Support Vector ◽

Svm Classifier ◽

Fundus Images ◽

Retinal Fundus Images ◽

Retinal Fundus

Automatic Detection and Classification of Diabetic Retinopathy from Retinal Fundus Images by Abdullah Biran, Master of Applied Science, lectrical and computer engineering Department, Ryerson University, 2017. Diabetic Retinopathy (DR) is an eye disease that leads to blindness when it progresses to proliferative level. The earliest signs of DR are the appearance of red and yellow lesions on the retina called hemorrhages and exudates. Early diagnosis of DR prevents from blindness. In this thesis, an automatic algorithm for detecting diabetic retinopathy is presented. The algorithm is based on combination of several image processing techniques including Circular Hough Transform (CHT), Contrast Limited Adaptive Histogram Equalization (CLAHE), Gabor filter and thresholding. In addition, Support Vector Machine (SVM) classifier is used to classify retinal images into normal or abnormal cases of DR including non-proliferative (NPDR) or proliferative diabetic retinopathy (PDR). The proposed method has been tested on fundus images from Standard Diabetic Retinopathy Database (DIARETDB). The implementation of the presented methodology was done in MATLAB. The methodology is tested for sensitivity and accuracy.

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Identification of Metabolic Pathway Dysregulation in Diabetic Retinopathy: A Propensity Score-Matched Metabolomic Study

10.21203/rs.3.rs-684065/v1 ◽

2021 ◽

Author(s):

Chengnan Guo ◽

Depeng Jiang ◽

Yixi Xu ◽

Fang Peng ◽

Shuzhen Zhao ◽

...

Keyword(s):

Diabetic Retinopathy ◽

Propensity Score ◽

Metabolic Pathway ◽

Univariate Analysis ◽

Support Vector ◽

Svm Classifier ◽

Diabetic Patients ◽

Lasso Regression ◽

Metabolic Alterations ◽

Pathway Dysregulation

Abstract Background Diabetic retinopathy (DR) is a major diabetes-related disease linked to metabolism. However, scientifically assessment of serum metabolic alterations in DR is scarce. We aimed to investigate the changes in metabolic coregulation from type 2 diabetic patients (T2DM) to DR and identify corresponding metabolite predictors via a widely targeted metabolomics approach.Methods In this case-control study, we tested 613 serum metabolites in 69 pairs of T2DM with DR (case) and propensity score-matched T2DM without DR (control) utilizing the ultra-performance liquid chromatography-electrospray ionization-tandem mass spectrometry system. The discrimination capability of differentially expressed metabolites (DEMs) in DR identification was also evaluated using a least absolute shrinkage and selection operator (LASSO) regression-based linear support vector machine (SVM) classifier. Metabolic pathway dysregulation in DR were comprehensively investigated by metabolic pathway analysis, chemical similarity enrichment analysis and MetaMapp approaches.Results A total of 89 DEMs were identified after paired univariate analysis and partial least squares discriminant analysis. The linear-SVM model based on LASSO regression selected DEMs had an excellent discrimination with an area under the ROC curve (AUC) as 0.99 (95% confidence interval: 0.95, 1.00). The biosynthesis of polyunsaturated fatty acids (PUFAs), thiamine metabolism, amino acids (mainly glycine, serine and threonine metabolism), hydroxyeicosatetraenoic acid (HETE), disaccharides, indoles and nucleotides were significantly enriched in DR. Conclusions This study systematically demonstrates that distinct metabolic alterations are linked to DR initiation. n-3 PUFAs, trehalose and vitamin B1 play an important role in inhibiting DR progression.

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A Machine Vision based Detection & Classification of Neovascularisation in Retinal Blood Vessels

International Journal of Innovative Technology and Exploring Engineering - Special Issue ◽

10.35940/ijitee.a1017.0881019 ◽

2019 ◽

Vol 8 (10) ◽

pp. 1468-1471

Keyword(s):

Diabetic Retinopathy ◽

Proliferative Diabetic Retinopathy ◽

Clustering Algorithm ◽

Automated System ◽

Support Vector ◽

Svm Classifier ◽

Retinal Blood Vessels ◽

Fuzzy C Means Clustering ◽

Sift Features

Diabetic Retinopathy is an eye disease which is caused by excessive sugar level in blood. Insufficient secretion of insulin hormone is the ground for evolution of diabetes. It affects most of the important organs in our body. There are two types of DR: Non Proliferative Diabetic Retinopathy and Proliferative Diabetic Retinopathy. In this proposed system techniques are introduced to detect and classify neovascularisation. Input fundus image is preprocessed by median filtering and further new vessels are segmented by using Fuzzy c-means clustering algorithm. After segmentation SIFT features are extracted and are used to train support vector machine (SVM) classifier. This automated system has been tested for 70 fundus images and accuracy of 96% is achieved

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Automatic detection and classification of diabetic retinopathy from retinal fundus images

10.32920/ryerson.14648760.v1 ◽

2021 ◽

Author(s):

Abdullah Biran

Keyword(s):

Diabetic Retinopathy ◽

Gabor Filter ◽

Automatic Detection ◽

Histogram Equalization ◽

Support Vector ◽

Svm Classifier ◽

Fundus Images ◽

Retinal Fundus Images ◽

Retinal Fundus

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Computer-Assisted Diagnosis for Diabetic Retinopathy Based on Fundus Images Using Deep Convolutional Neural Network

Mobile Information Systems ◽

10.1155/2019/6142839 ◽

2019 ◽

Vol 2019 ◽

pp. 1-14 ◽

Cited By ~ 10

Author(s):

Yung-Hui Li ◽

Nai-Ning Yeh ◽

Shih-Jen Chen ◽

Yu-Chien Chung

Keyword(s):

Neural Network ◽

Diabetic Retinopathy ◽

Convolutional Neural Network ◽

Recognition Rate ◽

Deep Convolutional Neural Network ◽

Support Vector ◽

Svm Classifier ◽

Computer Assisted ◽

Fundus Images ◽

Max Pooling

Diabetic retinopathy (DR) is a complication of long-standing diabetes, which is hard to detect in its early stage because it only shows a few symptoms. Nowadays, the diagnosis of DR usually requires taking digital fundus images, as well as images using optical coherence tomography (OCT). Since OCT equipment is very expensive, it will benefit both the patients and the ophthalmologists if an accurate diagnosis can be made, based solely on reading digital fundus images. In the paper, we present a novel algorithm based on deep convolutional neural network (DCNN). Unlike the traditional DCNN approach, we replace the commonly used max-pooling layers with fractional max-pooling. Two of these DCNNs with a different number of layers are trained to derive more discriminative features for classification. After combining features from metadata of the image and DCNNs, we train a support vector machine (SVM) classifier to learn the underlying boundary of distributions of each class. For the experiments, we used the publicly available DR detection database provided by Kaggle. We used 34,124 training images and 1,000 validation images to build our model and tested with 53,572 testing images. The proposed DR classifier classifies the stages of DR into five categories, labeled with an integer ranging between zero and four. The experimental results show that the proposed method can achieve a recognition rate up to 86.17%, which is higher than previously reported in the literature. In addition to designing a machine learning algorithm, we also develop an app called “Deep Retina.” Equipped with a handheld ophthalmoscope, the average person can take fundus images by themselves and obtain an immediate result, calculated by our algorithm. It is beneficial for home care, remote medical care, and self-examination.

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ANN and SVM to recognize Texture features for spontaneous Detection and Rating of Diabetic Retinopathy

International Journal of Engineering and Advanced Technology - Regular Issue ◽

10.35940/ijeat.a2203.109119 ◽

2019 ◽

Vol 9 (1) ◽

pp. 5590-5595

Keyword(s):

Diabetic Retinopathy ◽

Research Work ◽

Texture Features ◽

Support Vector ◽

Fundus Images ◽

Small Vessels ◽

The Neural Network ◽

Major Loss ◽

Occurrence Matrix ◽

Fluid Leak

The higher levels of blood glucose most often causes a metabolic disorder commonly called as Diabetes, scientifically as Diabetes Mellitus. A consequence of this is a major loss of vision and in long terms may eventually cause complete blindness. It initiates with swelling on blood vessels, formation of microaneurysms at the end of narrow capillaries. Haemorrhages due to rupture of small vessels and fluid leak causes exudates. The specialist examines it to diagnose and gives proper treatment. Fundus images are the fundamental tool for proper diagnosis of patients by medical experts. In this research work the fundus images are taken for processing, the neural network and support vector machine are trained for the proposed model. The features are extracted from the diabetic retinopathy image by using texture based algorithms such as Gabor, Local binary pattern and Gray level co-occurrence matrix for rating the level of diabetic retinopathy. The performance of all methods is calculated based on accuracy, precision, Recall and f-measure.

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PSO-SVM hybrid system for melanoma detection from histo-pathological images

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i4.pp2941-2949 ◽

2019 ◽

Vol 9 (4) ◽

pp. 2941

Author(s):

Maen Takruri ◽

Mohamed Khaled Abu Mahmoud ◽

Adel Al-Jumaily

Keyword(s):

Hybrid System ◽

Wavelet Packet ◽

Automated System ◽

Support Vector ◽

Svm Classifier ◽

Pathology Laboratory ◽

Swarm Optimization ◽

Image Histogram ◽

Melanoma Detection ◽

Occurrence Matrix

This paper introduces an automated system for skin cancer (melanoma) detection from Histo-pathological images sampled from microscopic slides of skin biopsy. The proposed system is a hybrid system based on Particle Swarm Optimization and Support Vector Machine (PSO-SVM). The features used are extracted from the grayscale image histogram, the co-occurrence matrix and the energy of the wavelet coefficients resulting from the wavelet packet decomposition. The PSO-SVM system selects the best feature set and the best values for the SVM parameters (C and γ) that optimize the performance of the SVM classifier. The system performance is tested on a real dataset obtained from the Southern Pathology Laboratory in Wollongong NSW, Australia. Evaluation results show a classification accuracy of 87.13%, a sensitivity of 94.1% and a specificity of 80.22%.The sensitivity and specificity results are comparable to those obtained by dermatologists.

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