Computational diagnosis of skin lesions from dermoscopic images using combined features

• Type: Article in International Scientific Journal
• Date: 2019-10

Authors

Roberta B. Oliveira (Author) Other; Aledir S. Pereira (Author) Other; João Manuel R. S. Tavares (Author) FEUP

Journal

Title: Neural Computing and Applications

Vol. 31

Pages: 6091-6111

ISSN: 0941-0643

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 10 Citations

ISI Web of Science

Scopus - 15 Citations

Scientific classification

FOS: Medical and Health sciences

CORDIS: Technological sciences

Other information

Authenticus ID: P-00N-SX1

DOI: 10.1007/s00521-018-3439-8

Abstract (EN): There has been an alarming increase in the number of skin cancer cases worldwide in recent years, which has raised interest in computational systems for automatic diagnosis to assist early diagnosis and prevention. Feature extraction to describe skin lesions is a challenging research area due to the difficulty in selecting meaningful features. The main objective of this work is to find the best combination of features, based on shape properties, colour variation and texture analysis, to be extracted using various feature extraction methods. Several colour spaces are used for the extraction of both colour- and texture-related features. Different categories of classifiers were adopted to evaluate the proposed feature extraction step, and several feature selection algorithms were compared for the classification of skin lesions. The developed skin lesion computational diagnosis system was applied to a set of 1104 dermoscopic images using a cross-validation procedure. The best results were obtained by an optimum-path forest classifier with very promising results. The proposed system achieved an accuracy of 92.3%, sensitivity of 87.5% and specificity of 97.1% when the full set of features was used. Furthermore, it achieved an accuracy of 91.6%, sensitivity of 87% and specificity of 96.2%, when 50 features were selected using a correlation-based feature selection algorithm.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21