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Computational methods for pigmented skin lesion classification in images: review and future trends

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Abstract

Skin cancer is considered as one of the most common types of cancer in several countries, and its incidence rate has increased in recent years. Melanoma cases have caused an increasing number of deaths worldwide, since this type of skin cancer is the most aggressive compared to other types. Computational methods have been developed to assist dermatologists in early diagnosis of skin cancer. An overview of the main and current computational methods that have been proposed for pattern analysis and pigmented skin lesion classification is addressed in this review. In addition, a discussion about the application of such methods, as well as future trends, is also provided. Several methods for feature extraction from both macroscopic and dermoscopic images and models for feature selection are introduced and discussed. Furthermore, classification algorithms and evaluation procedures are described, and performance results for lesion classification and pattern analysis are given.

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Fig. 1

(images publicly available from Bourne et al. [7])

Fig. 2

(images available in Argenziano et al. [76])

Fig. 3

(adapted from Celebi et al. [77]) (colour figure online)

Fig. 4
Fig. 5

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Notes

  1. These measures try to find the feature that may separate the classes as far as possible by greater distance between them.

  2. These measures establish the information gain from a feature.

  3. These measures are also known as correlation measures applied to evaluate the ability to predict the value of one feature from the value of another.

  4. These measures consist of finding a minimum number of features that may separate classes as consistently as the full set of features may.

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Acknowledgments

The first author would like to thank the CNPq (“Conselho Nacional de Desenvolvimento Científico e Tecnológico”), in Brazil, for her PhD grant. This work is funded by European Regional Development Funds (ERDF), through the Operational Programme “Thematic Factors of Competitiveness” (COMPETE), and Portuguese Funds, through “Fundação para a Ciência e a Tecnologia (FCT)”, under the Project: FCOMP-01-0124-FEDER-028160/PTDC/BBB-BMD/3088/2012. Authors gratefully acknowledge the funding of Project NORTE-01-0145-FEDER-000022—SciTech—Science and Technology for Competitive and Sustainable Industries, cofinanced by “Programa Operacional Regional do Norte” (NORTE2020), through “Fundo Europeu de Desenvolvimento Regional” (FEDER).

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Oliveira, R.B., Papa, J.P., Pereira, A.S. et al. Computational methods for pigmented skin lesion classification in images: review and future trends. Neural Comput & Applic 29, 613–636 (2018). https://doi.org/10.1007/s00521-016-2482-6

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