Next Article in Journal
An Extended Detailed Investigation of First and Second Order Supersymmetries for Off-Shell N = 2 and N = 4 Supermultiplets
Previous Article in Journal
Kinematic Skeleton Based Control of a Virtual Simulator for Military Training
Open AccessArticle

Symmetry Extraction in High Sensitivity Melanoma Diagnosis

1
Institute for Technological Development and Innovation in Communications (IDeTIC), University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria 35017, Spain
2
Head of Department of Dermatology, Hospital Universitario de Gran Canaria Doctor Negrín, Las Palmas de Gran Canaria 35010, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Michael Schlame
Symmetry 2015, 7(2), 1061-1079; https://doi.org/10.3390/sym7021061
Received: 5 March 2015 / Accepted: 9 June 2015 / Published: 15 June 2015
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. View Full-Text
Keywords: melanoma; asymmetry; machine learning; combined architecture; ABCD rule; Support Vector Machines melanoma; asymmetry; machine learning; combined architecture; ABCD rule; Support Vector Machines
Show Figures

MDPI and ACS Style

Guerra-Segura, E.; Travieso-González, C.M.; Alonso-Hernández, J.B.; Ravelo-García, A.G.; Carretero, G. Symmetry Extraction in High Sensitivity Melanoma Diagnosis. Symmetry 2015, 7, 1061-1079. https://doi.org/10.3390/sym7021061

AMA Style

Guerra-Segura E, Travieso-González CM, Alonso-Hernández JB, Ravelo-García AG, Carretero G. Symmetry Extraction in High Sensitivity Melanoma Diagnosis. Symmetry. 2015; 7(2):1061-1079. https://doi.org/10.3390/sym7021061

Chicago/Turabian Style

Guerra-Segura, Elyoenai; Travieso-González, Carlos M.; Alonso-Hernández, Jesús B.; Ravelo-García, Antonio G.; Carretero, Gregorio. 2015. "Symmetry Extraction in High Sensitivity Melanoma Diagnosis" Symmetry 7, no. 2: 1061-1079. https://doi.org/10.3390/sym7021061

Find Other Styles

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Search more from Scilit
 
Search
Back to TopTop