Breast Cancer Detection Based on Merging Four Modes MRI Using Convolutional Neural Networks

Abstract

The objective of the study is to develop a framework for automatic breast cancer detection with merging four imaging modes. Attempts were made for tumor classification and segmentation; using a multi-parametric Magnetic Resonance Imaging (MRI) method on breast tumors. MRI data of the breast were obtained from 67 subjects with a 1.5T-MRI scanner. Four imaging modes: were T1 weighted, T2 weighted, Diffusion Weighted and eTHRIVE sequences, and dynamic-contrast-enhanced(DCE)-MRI parameters are acquired. The proposed four-mode linkage backbone in tumor classification, which overcomes the limitations of single-modality image detection and simulates actual diagnosis processes by clinicians, achieves the accuracy of 0.942. The proposed automatic segmentation approach is performed by a refined U-Net architecture, and the result improved segmentation performance significantly. The combination of four-mode linkage classification backbone and improved segmentation network for breast cancer detection forms a computer-aided detection (CAD) system that corresponds to the actual clinical diagnosis work.