Cotton wool spots detection in diabetic retinopathy based on adaptive thresholding and ant colony optimization coupling support vector machine

2019 ◽  
Vol 14 (6) ◽  
pp. 884-893 ◽  
Author(s):  
Syna Sreng ◽  
Noppadol Maneerat ◽  
Kazuhiko Hamamoto ◽  
Ronakorn Panjaphongse
Keyword(s):  
Support Vector Machine ◽  
Diabetic Retinopathy ◽  
Ant Colony Optimization ◽  
Ant Colony ◽  
Adaptive Thresholding ◽  
Support Vector ◽  
Cotton Wool ◽  
Cotton Wool Spots ◽  
Coupling Support

Prediction of acute toxicity of emerging contaminants on the water flea Daphnia magna by Ant Colony Optimization–Support Vector Machine QSTR models

2017 ◽  
Vol 19 (3) ◽  
pp. 438-448 ◽  
Author(s):  
Reza Aalizadeh ◽  
Peter C. von der Ohe ◽  
Nikolaos S. Thomaidis
Keyword(s):  
Support Vector Machine ◽  
Acute Toxicity ◽  
Ant Colony Optimization ◽  
Daphnia Magna ◽  
Emerging Contaminants ◽  
Ant Colony ◽  
Support Vector ◽  
Water Flea

Prediction of acute toxicity towardsDaphnia magnausing Ant Colony Optimization–Support Vector Machine QSTR models.

Bloodless Technique to Detect Diabetes using Soft Computational Tool

2015 ◽  
pp. 139-158
Author(s):  
Puspalata Sah ◽  
Kandarpa Kumar Sarma
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Support Vector Machine ◽  
Support Vector ◽  
Cotton Wool ◽  
Research Issue ◽  
Important Research ◽  
Cotton Wool Spots ◽  
Essential Components ◽  
Soft Computing Techniques

Detection of diabetes using bloodless technique is an important research issue in the area of machine learning and artificial intelligence (AI). Here we present the working of a system designed to detect the abnormality of the eye with pain and blood free method. The typical features for diabetic retinopathy (DR) are used along with certain soft computing techniques to design such a system. The essential components of DR are blood vessels, red lesions visible as microaneurysms, hemorrhages and whitish lesions i.e., lipid exudates and cotton wool spots. The chapter reports the use of a unique feature set derived from the retinal image of the eye. The feature set is applied to a Support Vector Machine (SVM) which provides the decision regarding the state of infection of the eye. The classification ability of the proposed system for blood vessel and exudate is 91.67% and for optic disc and microaneurysm is 83.33%.

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