Skin Lesion Computational Diagnosis of Dermoscopic Images: Ensemble Models based on Input Feature Manipulation

• Tipo: Artigo em Revista Científica Internacional
• Data: 2017-10

Autores

Roberta B. Oliveira (Autor) Outra; Aledir S. Pereira (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Computer Methods and Programs in Biomedicine

Vol. 149

Páginas: 43-53

ISSN: 0169-2607

Editora: Elsevier

Indexação

ISI Web of Knowledge - 10 Citações

ISI Web of Science

Scopus

INSPEC

Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências da engenharia e tecnologias

Outras Informações

ID Authenticus: P-00M-YHY

DOI: 10.1016/j.cmpb.2017.07.009

Abstract (EN): Background and objectives: The number of deaths worldwide due to melanoma has risen in recent times, in part because melanoma is the most aggressive type of skin cancer. Computational systems have been developed to assist dermatologists in early diagnosis of skin cancer, or even to monitor skin lesions. However, there still remains a challenge to improve classifiers for the diagnosis of such skin lesions. The main objective of this article is to evaluate different ensemble classification models based on input feature manipulation to diagnose skin lesions. Methods: Input feature manipulation processes are based on feature subset selections from shape properties, colour variation and texture analysis to generate diversity for the ensemble models. Three subset selection models are presented here: (1) a subset selection model based on specific feature groups, (2) a correlation-based subset selection model, and (3) a subset selection model based on feature selection algorithms. Each ensemble classification model is generated using an optimum-path forest classifier and integrated with a majority voting strategy. The proposed models were applied on a set of 1104 dermoscopic images using a cross-validation procedure. Results: The best results were obtained by the first ensemble classification model that generates a feature subset ensemble based on specific feature groups. The skin lesion diagnosis computational system achieved 94.3% accuracy, 91.8% sensitivity and 96.7% specificity. Conclusions: The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11