Skin lesion computational diagnosis of dermoscopic images: Ensemble models based on input feature manipulation. (October 2017)

• Record Type: Journal Article
• Authors: Oliveira, Roberta B.; Pereira, Aledir S.; Tavares, João Manuel R.S.
• Is Part Of: Computer methods and programs in biomedicine. Volume 149(2017)
• Page Start: 43
• Page End: 53
• Publication Date: 2017-10
• Subjects: Image classification -- Feature extraction -- Feature selection -- Ensemble of classifiers -- Computational diagnosis; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.07.009
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: An effective computational diagnosis system for dermoscopic images. Ensemble classification models based on input feature manipulation. Feature subset selection from the shape properties, colour variation and texture analysis. Experiments focused on skin lesion classifications using ensemble algorithms. Abstract: 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.