A Novel CAD System for Breast DCE-MRI Based on Textural Analysis Using Several Machine Learning Methods

Abstract

Listen

Translate article

In this paper, we propose a Computer-Aided Detection (CAD) system based on the novel Multi-Classifier Fusion-based Classification Model (MCFM) of the breast lesions using textural features in Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI). The proposed system consists of four principal phases: Region Of Interest (ROI) segmentation, feature extraction, fusion and selection and finally Classification. We have extracted a complementary set of features from the DCE-MRI database on two statistical descriptors such as the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP) methods. Then, we have concatenated the extracted features using Serial method to exploit the complementary between these sets. Next, we have applied a features selection phase through Principal Component Analysis (PCA) and Feature Subset Selection (FSS) techniques to remove redundant information and improve the overall performance of breast DCE-MRI in a computer-aided system. For the classification, we have used a set of classifiers (K-Nearest Neighbors (K-NN), Support Vector Machines (SVM) and Multilayer Perceptron-Artificial Neural Network (MLP-ANN)). Then, a fusion Model between them using several metrics is applied. Experimental validation is performed over an MRI dataset, which is comprised of 286 patients with 143 Masses and 143 No Masses (Normal) breast tissues. These experiments report encouraging performances for mass detection, achieved using ten-fold cross-validation model, in terms of the averaged measurements of different metrics such as Sensitivity, Specificity, Area Under Curve (AUC) and Accuracy, which are 0.8972, 0.9572, 0.9630 and 0.9501, respectively. The proposed DCE-MRI analysis model can offer an efficient system for radiologists to identify the breast tissues.