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

Magnetic resonance imaging(MRI)is the most common post-diagnostic assessment of breast cancer.It is used to assess the risk of breast cancer disease progression.Mammary MRI can be used to determine the stage of breast cancer.Mammary MRI is mainly used to observe the size and extent of cancer tissues after breast cancer diagnosis,and help physicians to determine the stage of breast cancer and the changes of cancer tissues after the chemotherapy.The purpose of this research is to construct a model based on convolutional neural network that can automatically merge the four modes of MRI images at the same location of the breast to classify and segment the lesions of breast cancer.Attempts were made for tumor classi cation 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,Di usion Weighted and eTHRIVE sequences,and dynamiccontrast-enhanced(DCE)-MRI parameters are acquired.The proposed four-mode linkage backbone in tumor classi cation,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 re ned U-Net architecture,and the result improved segmentation performance signi cantly.The combination of four-mode linkage classi cation backbone and improved segmentation network for breast cancer detection forms a detection model based on convolutional neural network,and give a computer-aided detection(CAD)system that corresponds to the actual clinical diagnosis work.