Taxonomy of Diabetic Retinopathy Patients Using Biogeography-Based Optimization on Support Vector Machine Based on Digital Retinal Images

2021 ◽  
pp. 631-642
Author(s):  
N. Vinoth ◽  
M. Vijayakarthick ◽  
S. Ramesh ◽  
E. Sivaraman
Keyword(s):  
Support Vector Machine ◽  
Diabetic Retinopathy ◽  
Support Vector ◽  
Retinal Images

scholarly journals Improving Diabetic Diagnosis and Prevention with Machine Learning on Retinal Imaging

2021 ◽  
Vol 271 ◽  
pp. 01034
Author(s):  
Yushan Min
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Diabetic Retinopathy ◽  
Learning Algorithms ◽  
Area Under The Curve ◽  
Retinal Imaging ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Retinal Images ◽  
Support Vector Machine Algorithm

If the retinal images show evidences of abnormalities such as change in volume, diameter, and unusual spots in the retina, then there is a positive correlation to the diabetic progress. Mathematical and statistical theories behind the machine learning algorithms are powerful enough to detect signs of diabetes through retinal images. Several machine learning algorithms: Logistic Regression, Support Vector Machine, Random Forest, and Neural Networks were applied to predict whether images contain signs of diabetic retinopathy or not. After building the models, the computed results of these algorithms were compared by confusion matrixes, receiver operating characteristic curves, and Precision-Recall curves. The performance of the Support Vector Machine algorithm was the best since it had the highest true-positive rate, area under the curve for ROC curve, and area under the curve for Precision-Recall curve. This conclusion shows that the most complex algorithms doesn’t always give the best performance, the final accuracy also depends on the dataset. For this dataset of retinal imaging, the Support Vector Machine algorithm achieved the best results. Detecting signs of diabetic retinopathy is helpful for detecting for diabetes since more than 60% of patients with diabetes have signs of diabetic retinopathy. Machine learning algorithms can speed up the process and improve the accuracy of diagnosis. When the method is reliable enough, it can be utilized in diabetes diagnosis directly in clinics. Current methods require going on diets and taking blood samples, which could be very time consuming and inconvenient. Using machine learning algorithms is fast and noninvasive compared to the existing methods. The purpose of this research was to build an optimized model by machine learning algorithms that can improve the diagnosis accuracy and classification of patients at high risk of diabetes using retinal imaging.

scholarly journals PERBANDINGAN TINGKAT PENGENALAN CITRA DIABETIC RETINOPATHY PADA KOMBINASI PRINCIPLE COMPONENT DARI 4 CIRI BERBASIS METODE SVM (SUPPORT VECTOR MACHINE)

2016 ◽  
Vol 15 (1) ◽  
pp. 95-102
Author(s):  
Sari Ayu Wulandari ◽  
◽  
Rudy Tjahyono ◽  
Dian Retno Sawitri ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Diabetic Retinopathy ◽  
Support Vector ◽  
Principle Component

scholarly journals Sistem Klasifikasi Tingkat Keparahan Retinopati Diabetik Menggunakan Support Vector Machine

2018 ◽  
Vol 8 (1) ◽  
pp. 37 ◽  
Author(s):  
Taufiq Galang Adi Putranto ◽  
Ika Candradewi
Keyword(s):  
Support Vector Machine ◽  
Diabetic Retinopathy ◽  
Correlation Energy ◽  
Research Result ◽  
Texture Feature ◽  
Support Vector ◽  
Machine Method ◽  
Vision Disorder ◽  
Rbf Kernel ◽  
Occurrence Matrix

Diabetic retinopathy is a vision disorder disease that can cause damage to the retina of the eye that will have a direct impact on the disruption of vision of the patient. The diabetic retinopathy phase is classified into four types (normal, mild NPDR, moderate NPDR (Non-Proliferative Diabetic Retinopathy), and severe NPDR). Retinal of eye data of diabetic retinopathy patients treated from the MESSIDOR database. By applying image processing, the retinal image of the eye in extraction using the area features extraction from the detection of exudate, blood vessels, microaneurysms, and texture feature extraction Gray Level Co-occurrence Matrix. The extracted results classified using the Support Vector Machine method with the Radial Basis Function (RBF) kernel. Classification evaluated with these parameters: Accuracy, specificity, and sensitivity.The results of classification show the best value using 6 statistical features ie, contrast, homogeneity, correlation, energy, entropy and inverse difference moment in the direction of 45 degrees with the RBF kernel. The result of classification research system on 240 data training and 60 data testing yields an average accuracy is 95.93%, the value of specificity is 97.29%, and a sensitivity rating is  91.07%. From the research result, using RBF kernel get the best accuracy result than using kernel polynomial or kernel linear.

Sign in / Sign up

Export Citation Format

Share Document