Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier.
Prolonged diabetes retinopathy leads to diabetes maculopathy, which causes gradual and irreversible loss of vision. It is important for physicians to have a decision system that detects the early symptoms of the disease. This can be achieved by building a classification model using machine learning...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 53; no. 12; pp. 1345 - 1361 |
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| Autores principales: | , , , , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2015
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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=111243900&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 111243900 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2015 vid: 53 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 111243900 111243900 NLM26109519 10.1007/s11517-015-1329-0 NLM26109519 111243900 ppf: 1345 ppct: 16 formats: fmt: @attributes: type: P tig: atl: Classification of diabetes maculopathy images using data-adaptive neuro-fuzzy inference classifier. aug: au: Ibrahim, Sulaimon Chowriappa, Pradeep Dua, Sumeet Acharya, U. Noronha, Kevin Bhandary, Sulatha Mugasa, Hatwib Acharya, U Rajendra affil: Computer Science, Louisiana Tech University, Nethken Hall 121, 600 Dan Reneau Dr., #10348 Ruston 71272 USA sug: subj: Image Interpretation, Computer Assisted Methods Diabetic Retinopathy Classification Logic Neural Networks (Computer) Diabetic Retinopathy Diagnosis Cluster Analysis Algorithms ab: Prolonged diabetes retinopathy leads to diabetes maculopathy, which causes gradual and irreversible loss of vision. It is important for physicians to have a decision system that detects the early symptoms of the disease. This can be achieved by building a classification model using machine learning algorithms. Fuzzy logic classifiers group data elements with a degree of membership in multiple classes by defining membership functions for each attribute. Various methods have been proposed to determine the partitioning of membership functions in a fuzzy logic inference system. A clustering method partitions the membership functions by grouping data that have high similarity into clusters, while an equalized universe method partitions data into predefined equal clusters. The distribution of each attribute determines its partitioning as fine or coarse. A simple grid partitioning partitions each attribute equally and is therefore not effective in handling varying distribution amongst the attributes. A data-adaptive method uses a data frequency-driven approach to partition each attribute based on the distribution of data in that attribute. A data-adaptive neuro-fuzzy inference system creates corresponding rules for both finely distributed and coarsely distributed attributes. This method produced more useful rules and a more effective classification system. We obtained an overall accuracy of 98.55%. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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