%0 Conference Proceedings
%T Design of a System for Melanoma Detection Through the Processing of Clinical Images Using Artificial Neural Networks
%+ Faculty of Engineering [Bogotá]
%+ Universidad Nacional de Colombia [Bogotà] (UNAL)
%A Mahecha, Marco, Stiven Sastoque
%A Parra, Octavio, José Salcedo
%A Velandia, Julio, Barón
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 17th Conference on e-Business, e-Services and e-Society (I3E)
%C Kuwait City, Kuwait
%Y Salah A. Al-Sharhan
%Y Antonis C. Simintiras
%Y Yogesh K. Dwivedi
%Y Marijn Janssen
%Y Matti Mäntymäki
%Y Luay Tahat
%Y Issam Moughrabi
%Y Taher M. Ali
%Y Nripendra P. Rana
%I Springer International Publishing
%3 Challenges and Opportunities in the Digital Era
%V LNCS-11195
%P 605-616
%8 2018-10-30
%D 2018
%R 10.1007/978-3-030-02131-3_53
%K Neural networks
%K Deep learning
%K Clinical diagnosis
%K Patterns recognition
%Z Computer Science [cs]
%Z Computer Science [cs]/Networking and Internet Architecture [cs.NI]Conference papers
%X Skin cancer is one of the most important challenges in modern medicine, especially skin melanoma, being the main causer of deaths for this disease. Images analysis is one of the most transcendental techniques for Melanoma early detection as a prevention method. Artificial neural networks are one of the many developed techniques for images digital processing and characteristic similarities detection. In this work a graphic processing unit (GPU) is developed for clinical skin images analysis getting through an artificial neural networks system for similar patterns detection through processing in a collection of modules tasked of silhouette detection of the object to analyze into the image, and tasked to study borders or contour to determinate a final diagnostic, the dataset used for the training of the artificial neural network designed is gotten from the MED-NODE project and project of international skin images collaboration (ISIC) with 730 images of positive and negative cases as full, the proposed system presents finally an accuracy level of 76.67%, with a level of success of 78.79% in melanoma specific cases, and 74.07% in benign lesions cases.
%G English
%Z TC 6
%Z WG 6.11
%2 https://inria.hal.science/hal-02274187/document
%2 https://inria.hal.science/hal-02274187/file/474698_1_En_53_Chapter.pdf
%L hal-02274187
%U https://inria.hal.science/hal-02274187
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC6
%~ IFIP-WG6-11
%~ IFIP-I3E
%~ IFIP-LNCS-11195