Computer-Aided Diagnosis of Anterior Segment Eye Abnormalities using Visible Wavelength Image Analysis Based Machine Learning.
Eye disease is a major health problem among the elderly people. Cataract and corneal arcus are the major abnormalities that exist in the anterior segment eye region of aged people. Hence, computer-aided diagnosis of anterior segment eye abnormalities will be helpful for mass screening and grading in...
| Publicado en: | Journal of Medical Systems Vol. 42; no. 7; pp. 1 - 2 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2018
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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=130627134&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 130627134 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jul2018 vid: 42 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 130627134 130627134 130627134 10.1007/s10916-018-0980-z 130627134 ppf: 1 ppct: 1 formats: fmt: @attributes: type: P tig: atl: Computer-Aided Diagnosis of Anterior Segment Eye Abnormalities using Visible Wavelength Image Analysis Based Machine Learning. aug: au: S.V., Mahesh Kumar R., Gunasundari affil: Department of Electronics and Communication Engineering, Pondicherry Engineering College, Puducherry, India sug: subj: Cataract Diagnosis Corneal Diseases Diagnosis Diagnosis, Computer Assisted Machine Learning Human Descriptive Statistics Data Analysis Software P-Value Algorithms Analysis of Variance Sensitivity and Specificity Diagnostic Imaging ab: Eye disease is a major health problem among the elderly people. Cataract and corneal arcus are the major abnormalities that exist in the anterior segment eye region of aged people. Hence, computer-aided diagnosis of anterior segment eye abnormalities will be helpful for mass screening and grading in ophthalmology. In this paper, we propose a multiclass computer-aided diagnosis (CAD) system using visible wavelength (VW) eye images to diagnose anterior segment eye abnormalities. In the proposed method, the input VW eye images are pre-processed for specular reflection removal and the iris circle region is segmented using a circular Hough Transform (CHT)-based approach. The first-order statistical features and wavelet-based features are extracted from the segmented iris circle and used for classification. The Support Vector Machine (SVM) by Sequential Minimal Optimization (SMO) algorithm was used for the classification. In experiments, we used 228 VW eye images that belong to three different classes of anterior segment eye abnormalities. The proposed method achieved a predictive accuracy of 96.96% with 97% sensitivity and 99% specificity. The experimental results show that the proposed method has significant potential for use in clinical applications. pubtype: Academic Journal doctype: algorithm equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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