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

• Type: Article in International Scientific Journal
• Date: 2017-10

Authors

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

Journal

Title: Computer Methods and Programs in Biomedicine

Vol. 149

Pages: 43-53

ISSN: 0169-2607

Publisher: Elsevier

Indexing

ISI Web of Knowledge - 10 Citations

ISI Web of Science

Scopus

INSPEC

Scientific classification

CORDIS: Technological sciences

FOS: Engineering and technology

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11