Comparative analysis of classification based algorithms for diabetes diagnosis using iris images.
Photo-diagnosis is always an intriguing area for the researchers, with the advancement of image processing and computer machine vision techniques it have become more reliable and popular in recent years. The objective of this paper is to study the change in the features of iris, particularly irregul...
| Publicado en: | Journal of Medical Engineering & Technology Vol. 42; no. 1; pp. 35 - 43 |
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| Autores principales: | , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Taylor & Francis Ltd
Jan2018
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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=128003786&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 128003786 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 03091902 B9Q jtl: Journal of Medical Engineering & Technology issn: 03091902 maglogo: Y pubinfo: dt: Jan2018 vid: 42 iid: 1 pid: 377 pub: Taylor & Francis Ltd place: Philadelphia, Pennsylvania artinfo: ui: 128003786 128003786 NLM29300116 128003786 10.1080/03091902.2017.1412521 NLM29300116 128003786 ppf: 35 ppct: 8 formats: tig: atl: Comparative analysis of classification based algorithms for diabetes diagnosis using iris images. aug: au: Samant, Piyush Agarwal, Ravinder affil: Electrical and Instrumentation Engineering Department, Thapar University, Patiala, Punjab, India sug: subj: Image Interpretation, Computer Assisted Methods Diabetes Mellitus Iris Male Middle Age Signal Processing, Computer Assisted Algorithms Sensitivity and Specificity Female Middle Aged: 45-64 years Male Female ab: Photo-diagnosis is always an intriguing area for the researchers, with the advancement of image processing and computer machine vision techniques it have become more reliable and popular in recent years. The objective of this paper is to study the change in the features of iris, particularly irregularities in the pigmentation of certain areas of the iris with respect to diabetic health of an individual. Apart from the point that iris recognition concentrates on the overall structure of the iris, diagnostic techniques emphasises the local variations in the particular area of iris. Pre-image processing techniques have been applied to extract iris and thereafter, region of interest from the extracted iris have been cropped out. In order to observe the changes in the tissue pigmentation of region of interest, statistical, texture textural and wavelet features have been extracted. At the end, a comparison of accuracies of five different classifiers has been presented to classify two subject groups of diabetic and non-diabetic. Best classification accuracy has been calculated as 89.66% by the random forest classifier. Results have been shown the effectiveness and diagnostic significance of the proposed methodology. Presented piece of work offers a novel systemic perspective of non-invasive and automatic diabetic diagnosis. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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