SÁNCHEZ MONEDERO, JAVIER, Saez, Aurora , PÉREZ ORTIZ, MARÍA, Antonio Gutierrez, Pedro , Hervas-Martinez, Cesar
No
Lect. Notes Comput. Sci.
Proceedings Paper
Científica
0.339
01/01/2016
000389499600036
2-s2.0-84964047348
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.
Melanoma; Feature extraction; Dermoscopic image; Computer vision; Machine learning; Multi-class; Ordinal classification; Imbalanced classification