Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines.
Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on cl...
| Publicado en: | BioMed Research International Vol. 2016; pp. 1 - 9 |
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| Autor principal: | |
| Formato: | equations & formulas research tables/charts Journal Article |
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Wiley-Blackwell
6/13/2016
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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=116116091&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 116116091 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 6/13/2016 vid: 2016 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 116116091 116116091 116116091 10.1155/2016/4381972 116116091 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Computer-Aided Diagnosis of Micro-Malignant Melanoma Lesions Applying Support Vector Machines. aug: au: Jaworek-Korjakowska, Joanna affil: Department of Automatics and Biomedical Engineering, AGH University of Science and Technology, Aleja Mickiewicza 30, 30-059 Krakow, Poland sug: subj: Melanoma Diagnosis Diagnosis, Computer Assisted Technology Human Melanoma Mortality Skin Neoplasms Data Analysis Melanoma Physiopathology Funding Source ab: Background. One of the fatal disorders causing death is malignant melanoma, the deadliest form of skin cancer. The aim of the modern dermatology is the early detection of skin cancer, which usually results in reducing the mortality rate and less extensive treatment. This paper presents a study on classification of melanoma in the early stage of development using SVMs as a useful technique for data classification. Method. In this paper an automatic algorithm for the classification of melanomas in their early stage, with a diameter under 5 mm, has been presented. The system contains the following steps: image enhancement, lesion segmentation, feature calculation and selection, and classification stage using SVMs. Results. The algorithm has been tested on 200 images including 70 melanomas and 130 benign lesions. The SVM classifier achieved sensitivity of 90% and specificity of 96%. The results indicate that the proposed approach captured most of the malignant cases and could provide reliable information for effective skin mole examination. Conclusions. Micro-melanomas due to the small size and low advancement of development create enormous difficulties during the diagnosis even for experts. The use of advanced equipment and sophisticated computer systems can help in the early diagnosis of skin lesions. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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