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Multimedia Tools and Applications

, Volume 78, Issue 6, pp 6869–6888 | Cite as

ABCD rule and pre-trained CNNs for melanoma diagnosis

  • Nayara MouraEmail author
  • Rodrigo Veras
  • Kelson Aires
  • Vinícius Machado
  • Romuere Silva
  • Flávio Araújo
  • Maíla Claro
Article
  • 115 Downloads

Abstract

Skin cancer is the most common type of cancer and represents more than half of cancer diagnoses. Melanoma is the least frequent among skin cancers, but it is the most serious, with high potential for metastasis and can lead to death. However, melanoma is almost always curable if discovered in the early stages. In this context, computational methods for processing and analysis of skin lesion images have been studied and developed. This work proposes a computational approach to assist dermatologists in the diagnosis of skin lesions in melanoma or non-melanoma by means of dermoscopic images. The proposed methodology classifies skin lesions using a descriptor formed by the combination of the ABCD rule (Asymmetry, Border, Color, and Diameter) and pre-trained Convolutional Neural Networks (CNNs) features. The features were selected according to their gain ratios and used as input to the MultiLayer Perceptron classifier. We built a new database joining two distinct databases presented in the literature to validate the proposed methodology. The proposed method achieved an accuracy rate of 94.9% and Kappa index of 89.2%, which is considered “excellent”.

Keywords

Medical image classification ABCD rule Pre-trained CNNs Attribute selection Multilayer perceptron 

Notes

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Nayara Moura
    • 1
    Email author
  • Rodrigo Veras
    • 1
  • Kelson Aires
    • 1
  • Vinícius Machado
    • 1
  • Romuere Silva
    • 2
  • Flávio Araújo
    • 2
  • Maíla Claro
    • 1
  1. 1.Federal University of PiauíTeresinaBrazil
  2. 2.Federal University of PiauíPicosBrazil

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