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arXiv · 1807.07001

Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features

Russell C. Hardie·Redha Ali·Manawaduge Supun De Silva·Temesguen Messay Kebede
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Abstract

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.

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Russell C. Hardie, Redha Ali, Manawaduge Supun De Silva, Temesguen Messay Kebede. 2018-07-18. Skin Lesion Segmentation and Classification for ISIC 2018 Using Traditional Classifiers with Hand-Crafted Features. https://arxiv.org/abs/1807.07001

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