Melanoma detection in Images of Skin Lesions using Computer Vision and Deep Learning

Authors

  • Abraham Altuve Mouthon Universidad del Sinú
  • Elías Colón Muñoz Universidad del Sinú
  • Asael De la Rosa Sampayo Corporación Universitaria Rafael Núñez
  • Luis Murillo Fernández Universidad del Sinú
  • Luis Blanquicett Benavides Corporación Universitaria Rafael Núñez
  • Eugenia Arrieta Universidad del Sinú
  • Edward Núñez Valdés Universidad de Oviedo

DOI:

https://doi.org/10.17981/cesta.04.01.2023.05

Keywords:

Deep learning, skin lesions, melanoma, image processing, convolutional neural networks

Abstract

Introduction: The problem to be addressed in this work is the detection of melanoma, which is one of the different skin cancers that exist, which has a high mortality rate.

Objective: This document presents a research project in Artificial Intelligence whose objective is the detection of melanoma through image analysis using Deep learning.

Method: Initially, morphological operations are applied to the image to leave only the object of interest. This image is then fed into a convolutional neural network, which has been trained for melanoma detection.

Results:  The proposed convolutional network architecture presents acceptable results in the accuracy metric for the identification of malignant or bening melanoma. However, it is proposed to carry out future experiments that can improve these results.

Conclusions: Thanks to Deep Learning techniques with this class of tools, a very powerful and useful system is being offered when it comes to determining the diagnosis of this type of disease.

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Published

2023-07-13

How to Cite

Altuve Mouthon, A., Colón Muñoz, E., De la Rosa Sampayo, A., Murillo Fernández, L. ., Blanquicett Benavides, L., Arrieta, E., & Núñez Valdés, E. (2023). Melanoma detection in Images of Skin Lesions using Computer Vision and Deep Learning. Computer and Electronic Sciences: Theory and Applications, 4(1). https://doi.org/10.17981/cesta.04.01.2023.05

Issue

Vol. 4 No. 1 (2023)

Section

Artículos

License

Copyright (c) 2023 Abraham Altuve Mouthon, Elías Colón Muñoz, Asael De la Rosa Sampayo, Luis Murillo Fernández, Luis Blanquicett Benavides, Eugenia Arrieta, Edward Núñez Valdés

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