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Classification of melanoma presence and thickness based on computational image analysis

Sanchez-Monedero, Javier, Saez, Aurora, Perez-Ortiz, Maria, Antonio Gutierrez, Pedro and Hervas-Martinez, Cesar 2016. Classification of melanoma presence and thickness based on computational image analysis. Presented at: HAIS 2016: 11th International Conference on Hybrid Artificial Intelligence Systems, Seville, Spain, 18-20 April 2016. Hybrid Artificial Intelligent Systems: 11th International Conference, HAIS 2016, Seville, Spain, April 18-20, 2016, Proceedings. Lecture Notes in Computer Science Cham: Springer, pp. 427-438. 10.1007/978-3-319-32034-2_36

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Abstract

Melanoma is a type of cancer that occurs on the skin. Only in the US, 50,000–100,000 patients are yearly diagnosed with melanoma. Five year survival rate highly depends on early detection, varying between 99 % and 15 % depending on the melanoma stage. Melanoma is typically identified with a visual inspection and lately confirmed and classified by a biopsy. In this work, we propose a hybrid system combining features which describe melanoma images together with machine learning models that learn to distinguish melanoma lesions. Although previous works distinguish melanoma and non-melanoma images, those works focus only in the binary case. Opposed to this, we propose to consider finer classification levels within a five class learning problem. We evaluate the performance of several nominal and ordinal classifiers using four performance metrics to provide highlights of several aspects of classification performance, achieving promising results.

Item Type: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Journalism, Media and Culture
Publisher: Springer
ISBN: 978-3-319-32034-2
ISSN: 0302-9743
Last Modified: 26 Jul 2019 10:11
URI: http://orca.cf.ac.uk/id/eprint/112800

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