Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence.
Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 18 |
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| Autores principales: | , |
| Formato: | algorithm equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
1/17/2016
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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=113630325&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 113630325 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 1/17/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 113630325 113630325 113630325 10.1155/2016/8934242 113630325 ppf: 1 ppct: 17 formats: fmt: @attributes: type: P tig: atl: Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence. aug: au: Jaworek-Korjakowska, Joanna Kłeczek, Paweł affil: Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Aleja Mickiewicza 30, 30-059 Krakow, Poland sug: subj: Artificial Intelligence Data Analysis, Computer Assisted Melanoma Classification Human Sensitivity and Specificity Algorithms Funding Source Descriptive Statistics Data Analysis Software ab: Background. Given its propensity to metastasize, and lack of effective therapies for most patients with advanced disease, early detection of melanoma is a clinical imperative. Different computer-aided diagnosis (CAD) systems have been proposed to increase the specificity and sensitivity of melanoma detection. Although such computer programs are developed for different diagnostic algorithms, to the best of our knowledge, a system to classify different melanocytic lesions has not been proposed yet. Method. In this research we present a new approach to the classification of melanocytic lesions. This work is focused not only on categorization of skin lesions as benign or malignant but also on specifying the exact type of a skin lesion including melanoma, Clark nevus, Spitz/Reed nevus, and blue nevus. The proposed automatic algorithm contains the following steps: image enhancement, lesion segmentation, feature extraction, and selection as well as classification. Results. The algorithm has been tested on 300 dermoscopic images and achieved accuracy of 92% indicating that the proposed approach classified most of the melanocytic lesions correctly. Conclusions. A proposed system can not only help to precisely diagnose the type of the skin mole but also decrease the amount of biopsies and reduce the morbidity related to skin lesion excision. 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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