Symmetry Extraction in High Sensitivity Melanoma Diagnosis

Symmetry

Elyoenai Guerra-Segura¹  Carlos M. Travieso-González¹  Jesús B. Alonso-Hernández¹  Antonio G. Ravelo-Garc໚¹  Gregorio Carretero² 

[1] Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35017, Spain; E-Mails:;Head of Department of Dermatology, Hospital Universitario de Gran Canaria Doctor Negrín, Las Palmas de Gran Canaria 35010, Spain; E-Mail:

关键词: melanoma;    asymmetry;    machine learning;    combined architecture;    ABCD rule;    Support Vector Machines;   

DOI  :  10.3390/sym7021061

来源: mdpi

【 摘 要 】

Melanoma diagnosis depends on the experience of doctors. Symmetry is one of the most important factors to measure, since asymmetry shows an uncontrolled growth of cells, leading to melanoma cancer. A system for melanoma detection in diagnosing melanocytic diseases with high sensitivity is proposed here. Two different sets of features are extracted based on the importance of the ABCD rule and symmetry evaluation to develop a new architecture. Support Vector Machines are used to classify the extracted sets by using both an alternative labeling method and a structure divided into two different classifiers which prioritize sensitivity. Although feature extraction is based on former works, the novelty lies in the importance given to symmetry and the proposed architecture, which combines two different feature sets to obtain a high sensitivity, prioritizing the medical aspect of diagnosis. In particular, a database provided by Hospital Universitario de Gran Canaria Doctor Negrín was tested, obtaining a sensitivity of 100% and a specificity of 66.66% using a leave-one-out validation method. These results show that 66.66% of biopsies would be avoided if this system is applied to lesions which are difficult to classify by doctors.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland