Feature Selection and Parameters Optimization of Support Vector Machines Based on Hybrid Glowworm Swarm Optimization for Classification of Diabetic Retinopathy.
Diabetic Retinopathy (DR) has been a leading cause of blindness in case of human beings falling between the ages of 20 and 74 years. This will have a major influence on both the patient and the society as it can normally influence the humans in their gainful years. An early DR detection is quite cha...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 10; pp. 1 - 2 |
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| Autores principales: | , |
| Formato: | algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Oct2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=132085524&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 132085524 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Oct2018 vid: 42 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 132085524 132085524 132085524 10.1007/s10916-018-1055-x 132085524 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Feature Selection and Parameters Optimization of Support Vector Machines Based on Hybrid Glowworm Swarm Optimization for Classification of Diabetic Retinopathy. aug: au: Karthikeyan, R. Alli, P. affil: Department of CSE, PSNA College of Engineering and Technology, Dindigul, Tamilnadu, India sug: subj: Technology, Medical Diabetic Retinopathy Diagnosis Algorithms Image Processing, Computer Assisted Human Genetics Microaneurysm Radiography Retina Radiography Blood Vessels Radiography Exudates and Transudates Tomography, Optical Coherence Angiography Sensitivity and Specificity Descriptive Statistics ab: Diabetic Retinopathy (DR) has been a leading cause of blindness in case of human beings falling between the ages of 20 and 74 years. This will have a major influence on both the patient and the society as it can normally influence the humans in their gainful years. An early DR detection is quite challenging as it may not be detected by humans. There are several techniques and algorithms that have been established for detecting the DR. These techniques have been facing problems to achieve effective sensitivity, accuracy, and specificity. In order to overcome all these problems, the work has proposed one more such effective algorithm for image processing in order to increase the efficiency and also identify easily the DR diseases. A major challenge in the task is the automatic detection of the microaneurysms. In this work, the Support Vector Machine (SVM) parameters optimized with Glowworm Swarm Optimization (GSO) and Genetic Algorithm (GA) is used to classify the DR. Because the SVM parameter C and γ to control the performance of the classifier. For this work, the SVMs get fused with the hybrid GSO-GA along with the feature chromosomes that are generated that will thereby direct the GA search to a straight line of the error of optimal generalization in their super parameter space. This GSO algorithm will not have memory and the glow worms will not retain any information in memory. The results of the experiment prove that this method had achieved a better performance. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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