Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images.
Diabetic retinopathy (DR) is a leading cause of vision loss among diabetic patients in developed countries. Early detection of occurrence of DR can greatly help in effective treatment. Unfortunately, symptoms of DR do not show up till an advanced stage. To counter this, regular screening for DR is e...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 52; no. 8; pp. 663 - 673 |
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| Autores principales: | , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Aug2014
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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=103834553&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103834553 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Aug2014 vid: 52 iid: 8 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103834553 NLM24958614 2012651624 10.1007/s11517-014-1167-5 NLM24958614 103834553 ppf: 663 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Computer-aided diabetic retinopathy detection using trace transforms on digital fundus images. aug: au: Ganesan, Karthikeyan Martis, Roshan Joy Acharya, U Rajendra Chua, Chua Kuang Min, Lim Choo Ng, E Y K Laude, Augustinus affil: Department of ECE, Ngee Ann Polytechnic, Clementi Road, Clementi, 599489, Singapore, g.karthikeya@gmail.com. sug: subj: Diabetic Retinopathy Diagnosis Diagnosis, Computer Assisted Methods Retina Image Processing, Computer Assisted Adult Middle Age Neural Networks (Computer) Algorithms Young Adult Adult: 19-44 years Middle Aged: 45-64 years ab: Diabetic retinopathy (DR) is a leading cause of vision loss among diabetic patients in developed countries. Early detection of occurrence of DR can greatly help in effective treatment. Unfortunately, symptoms of DR do not show up till an advanced stage. To counter this, regular screening for DR is essential in diabetic patients. Due to lack of enough skilled medical professionals, this task can become tedious as the number of images to be screened becomes high with regular screening of diabetic patients. An automated DR screening system can help in early diagnosis without the need for a large number of medical professionals. To improve detection, several pattern recognition techniques are being developed. In our study, we used trace transforms to model a human visual system which would replicate the way a human observer views an image. To classify features extracted using this technique, we used support vector machine (SVM) with quadratic, polynomial, radial basis function kernels and probabilistic neural network (PNN). Genetic algorithm (GA) was used to fine tune classification parameters. We obtained an accuracy of 99.41 and 99.12% with PNN-GA and SVM quadratic kernels, respectively. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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