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In this article, we present a multi\u2010classifier system for improving the diagnostic accuracy of melanoma and dysplastic lesions based on the decision template combination rule.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>First, the lesion is differentiated from the surrounding healthy skin in an image. Next, shape, colour and texture features are extracted from the lesion image. Different subsets of these features are fed to three different classifiers: k\u2010nearest neighbour (k\u2010<jats:styled-content style=\"fixed-case\">NN<\/jats:styled-content>), support vector machine (<jats:styled-content style=\"fixed-case\">SVM<\/jats:styled-content>) and linear discriminant analysis (<jats:styled-content style=\"fixed-case\">LDA<\/jats:styled-content>). The decision template method is used to combine the outputs of these classifiers.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The proposed method has been evaluated on a set of 436 dermatoscopic images of benign, dysplastic and melanoma lesions. The final classifier ensemble delivers a total classification accuracy of 80.46%, with 67.73% of dysplastic lesions correctly classified and 83.53% of melanoma lesions correctly classified.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>The results show that the proposed method significantly increases the diagnostic accuracy of dysplastic and melanoma lesions compared with a single classifier. The total classification rate is also improved.<\/jats:p><\/jats:sec>","DOI":"10.1111\/j.1600-0846.2012.00617.x","type":"journal-article","created":{"date-parts":[[2012,6,6]],"date-time":"2012-06-06T03:05:23Z","timestamp":1338951923000},"source":"Crossref","is-referenced-by-count":15,"title":["Improving the diagnostic accuracy of dysplastic and melanoma lesions using the decision template combination method"],"prefix":"10.1111","volume":"19","author":[{"given":"Maryam","family":"Faal","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering Biomedical Engineering Group Tarbiat Modares University Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mohammad Hossein","family":"Miran Baygi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering Biomedical Engineering Group Tarbiat Modares University Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ehsanollah","family":"Kabir","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering Electronic Engineering Group Tarbiat Modares University Tehran Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2012,6,5]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.3322\/caac.20074"},{"key":"e_1_2_10_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2010.06.016"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.compmedimag.2008.11.002"},{"key":"e_1_2_10_5_1","unstructured":"Melanoma Skin Cancer.American Cancer Society website. 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