Dynamic contrast enhanced-magnetic resonance imaging radiomics combined with a hybrid adaptive neuro-fuzzy inference system-particle swarm optimization approach for breast tumour classification

Abstract

The authors propose a method for breast dynamic contrast enhanced-magnetic resonance imaging classification by combining radiomic texture analysis with a hybrid adaptive neuro-fuzzy inference system (ANFIS)-particle swarm optimization (PSO) classifier. The fast discrete curvelet transform is utilized as a decomposition scheme in multiple scales. The mean and entropy features extracted from the produced scheme are used as texture descriptors. Principal component analysis (PCA) involves reduction of the dimensionality of the initial feature set. The transformed feature vector is subsequently introduced to a hybrid ANFIS-PSO classifier. The average overall classification power of the proposed hybrid ANFIS-PSO classifier is comparatively assessed to that obtained using several classifiers (ANFIS, linear discriminant analysis, Naïve Bayes, artificial neural networks, random forest and support vector machine) by using the 70 training-30 testing data ratio. The comparison performed highlights the superiority of the proposed methodology, thus underlying the potential of ANFIS-PSO for the breast cancer diagnosis with a classification accuracy of 94%.