@inproceedings{be54a47ad4b44cb6942cefe763fb5a5e,
title = "Color Models for Skin Lesion Classification from Dermatoscopic Images",
abstract = "In this paper, we present an architecture for classification of pigmented skin lesions from dermatoscopic images. The architecture is using image preprocessing for natural hair removal and image segmentation for extraction of the skin lesion area followed by computation of statistical values of colors as features. The color-based features were extracted from several well-known and widely used color models. Several classification algorithms were evaluated with the best performing classification algorithm being the AdaBoost with random forest classifier with classification accuracy equal to 73.08% when using RGB-based features only and 74.26% when combining RGB, HSV, and YIQ color model-based features.",
keywords = "Color models, Dermatoscopy, Image classification",
author = "Iosif Mporas and Isidoros Perikos and Michael Paraskevas",
year = "2020",
month = jan,
day = "1",
doi = "10.1007/978-981-15-1918-5_5",
language = "English",
isbn = "9789811519178",
series = "Smart Innovation, Systems and Technologies",
publisher = "Springer Nature ",
pages = "85--98",
editor = "Ioannis Hatzilygeroudis and Isidoros Perikos and Foteini Grivokostopoulou",
booktitle = "Advances in Integrations of Intelligent Methods - Post-workshop volume of the 8th International Workshop CIMA 2018, in conjunction with IEEE ICTAI 2018",
address = "Netherlands",
note = "8th International Workshop on Combinations of Intelligent Methods and Applications, CIMA 2018 held in conjunction with the 30th IEEE International Conference on Tools with Artificial Intelligence, IEEE ICTAI 2018 ; Conference date: 05-11-2018 Through 07-11-2018",
}