Detection of skin cancer 'Melanoma' through computer vision

Wilson F. Cueva; F. Munoz; G. Vasquez; G. Delgado

doi:10.1109/INTERCON.2017.8079674

Detection of skin cancer 'Melanoma' through computer vision

Wilson F. Cueva
, F. Munoz
, G. Vasquez
, G. Delgado

Universidad del Azuay

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

20 Scopus citations

Abstract

In the last decades, skin cancer increased its incidence becoming a public health problem. Technological advances have allowed the development of applications that help the early detection of melanoma. In this context, an image processing was developed to obtain Asymmetry, Border, Color, and Diameter (ABCD of melanoma). Using neural networks to perform a classification of the different kinds of moles. As a result, this algorithm developed after an analysis of 200 images was obtained a performance of 97.51%.

Original language	English
Title of host publication	Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509063628
DOIs	https://doi.org/10.1109/INTERCON.2017.8079674
State	Published - 20 Oct 2017
Externally published	Yes
Event	24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 - Cusco, Peru Duration: 15 Aug 2017 → 18 Aug 2017

Publication series

Name	Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

Conference

Conference	24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017
Country/Territory	Peru
City	Cusco
Period	15/08/17 → 18/08/17

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

Artificial Intelligence
Image Processing
Melanoma
Neural Networks

Access to Document

10.1109/INTERCON.2017.8079674

Cite this

Cueva, W. F., Munoz, F., Vasquez, G., & Delgado, G. (2017). Detection of skin cancer 'Melanoma' through computer vision. In Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 Article 8079674 (Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/INTERCON.2017.8079674

Cueva, Wilson F. ; Munoz, F. ; Vasquez, G. et al. / Detection of skin cancer 'Melanoma' through computer vision. Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017. Institute of Electrical and Electronics Engineers Inc., 2017. (Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017).

@inproceedings{67eb8ba9085c4ea684297a9e311ba712,

title = "Detection of skin cancer 'Melanoma' through computer vision",

abstract = "In the last decades, skin cancer increased its incidence becoming a public health problem. Technological advances have allowed the development of applications that help the early detection of melanoma. In this context, an image processing was developed to obtain Asymmetry, Border, Color, and Diameter (ABCD of melanoma). Using neural networks to perform a classification of the different kinds of moles. As a result, this algorithm developed after an analysis of 200 images was obtained a performance of 97.51\%.",

keywords = "Artificial Intelligence, Image Processing, Melanoma, Neural Networks",

author = "Cueva, \{Wilson F.\} and F. Munoz and G. Vasquez and G. Delgado",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017 ; Conference date: 15-08-2017 Through 18-08-2017",

year = "2017",

month = oct,

day = "20",

doi = "10.1109/INTERCON.2017.8079674",

language = "Ingl{\'e}s",

series = "Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017",

}

Cueva, WF, Munoz, F, Vasquez, G & Delgado, G 2017, Detection of skin cancer 'Melanoma' through computer vision. in Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017., 8079674, Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017, Institute of Electrical and Electronics Engineers Inc., 24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017, Cusco, Peru, 15/08/17. https://doi.org/10.1109/INTERCON.2017.8079674

Detection of skin cancer 'Melanoma' through computer vision. / Cueva, Wilson F.; Munoz, F.; Vasquez, G. et al.
Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017. Institute of Electrical and Electronics Engineers Inc., 2017. 8079674 (Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detection of skin cancer 'Melanoma' through computer vision

AU - Cueva, Wilson F.

AU - Munoz, F.

AU - Vasquez, G.

AU - Delgado, G.

PY - 2017/10/20

Y1 - 2017/10/20

N2 - In the last decades, skin cancer increased its incidence becoming a public health problem. Technological advances have allowed the development of applications that help the early detection of melanoma. In this context, an image processing was developed to obtain Asymmetry, Border, Color, and Diameter (ABCD of melanoma). Using neural networks to perform a classification of the different kinds of moles. As a result, this algorithm developed after an analysis of 200 images was obtained a performance of 97.51%.

AB - In the last decades, skin cancer increased its incidence becoming a public health problem. Technological advances have allowed the development of applications that help the early detection of melanoma. In this context, an image processing was developed to obtain Asymmetry, Border, Color, and Diameter (ABCD of melanoma). Using neural networks to perform a classification of the different kinds of moles. As a result, this algorithm developed after an analysis of 200 images was obtained a performance of 97.51%.

KW - Artificial Intelligence

KW - Image Processing

KW - Melanoma

KW - Neural Networks

UR - https://www.scopus.com/pages/publications/85039989899

U2 - 10.1109/INTERCON.2017.8079674

DO - 10.1109/INTERCON.2017.8079674

M3 - Contribución a la conferencia

AN - SCOPUS:85039989899

T3 - Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

BT - Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 24th IEEE International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017

Y2 - 15 August 2017 through 18 August 2017

ER -

Cueva WF, Munoz F, Vasquez G, Delgado G. Detection of skin cancer 'Melanoma' through computer vision. In Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017. Institute of Electrical and Electronics Engineers Inc. 2017. 8079674. (Proceedings of the 2017 IEEE 24th International Congress on Electronics, Electrical Engineering and Computing, INTERCON 2017). doi: 10.1109/INTERCON.2017.8079674

Detection of skin cancer 'Melanoma' through computer vision

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Cite this