This paper provides the required description of the methods used to obtain submitted results for Task1 and Task 3 of ISIC 2018: Skin Lesion Analysis Towards Melanoma Detection. The results have been created by a team of researchers at the University of Dayton Signal and Image Processing Lab. In this submission, traditional classifiers with hand-crafted features are utilized for Task 1 and Task 3. Our team is providing additional separate submissions using deep learning methods for comparison.
Cite
@article{arxiv.1807.07001,
title = {Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features},
author = {Russell C. Hardie and Redha Ali and Manawaduge Supun De Silva and Temesguen Messay Kebede},
journal= {arXiv preprint arXiv:1807.07001},
year = {2018}
}