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Neural Computing and Applications

Erschienen in:

15.07.2016 | Review

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

verfasst von: Roberta B. Oliveira, João P. Papa, Aledir S. Pereira, João Manuel R. S. Tavares

Erschienen in: Neural Computing and Applications | Ausgabe 3/2018

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

These measures establish the information gain from a feature.

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.

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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Titel: Computational methods for pigmented skin lesion classification in images: review and future trends
verfasst von: Roberta B. Oliveira
João P. Papa
Aledir S. Pereira
João Manuel R. S. Tavares
Publikationsdatum: 15.07.2016
Verlag: Springer London
Erschienen in: Neural Computing and Applications / Ausgabe 3/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-016-2482-6

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