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- W2753600281 abstract "This paper describes ontology-based semantic analysis of lesion images. We first present our ontology focusing on its main concepts, as well as the semantic annotation. Accordingly, the Bag-of-Words (BoW), modeling these concepts in skin lesion diagnosis, is inspired from experts in dermatology. These BoWs are modeled from the lesion images. Firstly, we extract low-level features describing the lesion shape, color and texture. Secondly, the BoWs are generated from these features using a machine learning classifier (SVM). An important step in semantic analysis is to define rules relating the different concepts. In our case, these rules are inspired from the score of the ABCD rule for decision making. Experimental results on a public database of 206 lesion images demonstrate that ontology offers a more efficient frame of analysis, where semantic relations between concepts can handle more knowledge of experts, and can be more appropriate for lesion severity classification with a good accuracy. Comparing to the previous works, our approach yields good sensitivity (97.4%) and accuracy (76.9%)." @default.
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- W2753600281 date "2017-01-01" @default.
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- W2753600281 title "Automatic Skin Lesions Classification Using Ontology-Based Semantic Analysis of Optical Standard Images" @default.
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- W2753600281 doi "https://doi.org/10.1016/j.procs.2017.08.226" @default.
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