Color space influence on ANN skin lesion classification using statistics texture feature

  • Felicia Anisoara Damian Dunarea de Jos University of Galati
  • Simona Moldovanu Dunarea de Jos University of Galati
  • Luminita Moraru Dunarea de Jos University of Galati
DOI: https://doi.org/10.35219/ann-ugal-math-phys-mec.2021.1.08
Keywords: RGB, YIQ, skewness, kurtosis, Feed-Forward Back Propagation Network (FFBPN)

Abstract

This study aims to investigate the ability of an artificial neural network to differentiate between malign and benign skin lesions based on two statistics terms and for RGB (R red, G green, B blue) and YIQ (Y luminance, and I and Q chromatic differences) color spaces. The targeted statistics texture features are skewness (S) and kurtosis (K) which are extracted from the histograms of each color channel corresponding to the color spaces and for the two classes of lesions: nevi and melanomas. The extracted data is used to train the Feed-Forward Back Propagation Networks (FFBPNs). The number of neurons in the hidden layer varies: it can be 8, 16, 24, or 32. The results indicate skewness features computed for the red channel in the RGB color space as the best choice to reach the goal of our study. The reported result shows the advantages of monochrome channels representation for skin lesions diagnosis.

Published
2021-11-12
How to Cite
Damian, F., Moldovanu, S. and Moraru, L. (2021) “Color space influence on ANN skin lesion classification using statistics texture feature”, Analele Universității ”Dunărea de Jos” din Galați. Fascicula II, Matematică, fizică, mecanică teoretică / Annals of the ”Dunarea de Jos” University of Galati. Fascicle II, Mathematics, Physics, Theoretical Mechanics, 44(1), pp. 53-62. doi: https://doi.org/10.35219/ann-ugal-math-phys-mec.2021.1.08.
Issue
Vol 44 No 1 (2021)
Section
Articles

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