@inproceedings{d9f8829898bf406caa44a206c54528e6,
title = "Pigmented Skin Lesions Classification Using Data Driven Subsets of Image Features",
abstract = "In this paper we present an architecture for identification of pigmented skin lesions from dermatoscopic images. The architecture used a large number of image features and was evaluated with several classification algorithms on different feature subsets as extracted from feature ranking. The best performing classification algorithm was the support vector machines using polynomial kernel function with classification accuracy equal to 74.69% and the most precisely classified skin lesion type between seven different skin pathologies was nevus with accuracy equal to 94.38%.",
keywords = "Dermatoscopy, Feature selection, Image classification",
author = "Iosif Mporas and Isidoros Perikos and Michael Paraskevas",
year = "2019",
month = jul,
doi = "10.1109/IISA.2019.8900769",
language = "English",
series = "10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
booktitle = "10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019",
address = "United States",
note = "10th International Conference on Information, Intelligence, Systems and Applications, IISA 2019 ; Conference date: 15-07-2019 Through 17-07-2019",
}