Oliveira Roberta B, Pereira Aledir S, Tavares João Manuel R S
Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, rua Dr. Roberto Frias, Porto 4200-465, Portugal.
Departamento de Ciências de Computação e Estatística, Instituto de Biociências, Letras e Ciências Exatas, Universidade Estadual Paulista, rua Cristóvão Colombo, 2265, São José do Rio Preto, SP 15054-000, Brazil.
Comput Methods Programs Biomed. 2017 Oct;149:43-53. doi: 10.1016/j.cmpb.2017.07.009. Epub 2017 Jul 20.
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.
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.
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.
The input feature manipulation process based on specific feature subsets generated the greatest diversity for the ensemble classification model with very promising results.
近年来，全球因黑色素瘤导致的死亡人数有所上升，部分原因是黑色素瘤是最具侵袭性的皮肤癌类型。已开发出计算系统来协助皮肤科医生进行皮肤癌的早期诊断，甚至监测皮肤病变。然而，改进此类皮肤病变诊断的分类器仍面临挑战。本文的主要目的是基于输入特征操作评估不同的集成分类模型，以诊断皮肤病变。
输入特征操作过程基于从形状属性、颜色变化和纹理分析中进行特征子集选择，为集成模型生成多样性。这里提出了三种子集选择模型：（1）基于特定特征组的子集选择模型，（2）基于相关性的子集选择模型，以及（3）基于特征选择算法的子集选择模型。每个集成分类模型使用最优路径森林分类器生成，并与多数投票策略相结合。所提出的模型通过交叉验证程序应用于一组1104张皮肤镜图像。
基于特定特征组生成特征子集成的第一个集成分类模型取得了最佳结果。皮肤病变诊断计算系统的准确率达到94.3%，灵敏度为91.8%，特异性为96.7%。
基于特定特征子集的输入特征操作过程为集成分类模型生成了最大的多样性，结果非常有前景。
