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• W2286378748 abstract "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.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.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.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." @default.
• W2286378748 created "2016-06-24" @default.
• W2286378748 creator A5000148255 @default.
• W2286378748 creator A5068686726 @default.
• W2286378748 date "2016-01-01" @default.
• W2286378748 modified "2023-10-12" @default.
• W2286378748 title "Automatic Classification of Specific Melanocytic Lesions Using Artificial Intelligence" @default.
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• W2286378748 doi "https://doi.org/10.1155/2016/8934242" @default.
• W2286378748 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/4739011" @default.
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• W2286378748 hasPublicationYear "2016" @default.
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