Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification.

Journal: Academic radiology
Published Date: Jan 10, 2023

Abstract

RATIONALE AND OBJECTIVES: Diagnosis of breast cancer on MRI requires, first, the identification of suspicious lesions; second, the characterization to give a diagnostic impression. We implemented Mask Reginal-Convolutional Neural Network (R-CNN) to detect abnormal lesions, followed by ResNet50 to estimate the malignancy probability.

Authors

  • Yang Zhang
    Innovative Institute of Chinese Medicine and Pharmacy, Academy for Interdiscipline, Chengdu University of Traditional Chinese Medicine, Chengdu, China.
  • Yan-Lin Liu
    Department of Radiological Sciences, University of California, 164 Irvine Hall, Irvine, CA, 92697-5020, USA.
  • Ke Nie
    Department of Radiation Oncology, Rutgers-Cancer Institute of New Jersey, Rutgers-Robert Wood Johnson Medical School, New Brunswick, NJ, United States.
  • Jiejie Zhou
    Department of Radiology, First Affiliate Hospital of Wenzhou Medical University, Wenzhou, China.
  • Zhongwei Chen
    Department of Radiology, First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China.
  • Jeon-Hor Chen
    Department of Radiology, E-Da Hospital and I-Shou University, Kaohsiung 82445, Taiwan and Tu and Yuen Center for Functional Onco-Imaging and Department of Radiological Science, University of California, Irvine, California 92697.
  • Xiao Wang
    Research Centre of Basic Integrative Medicine, School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China.
  • Bomi Kim
    KIN Center for Digital Innovation, School of Business and Economics, Vrije Universiteit Amsterdam, De Boelelaan 1105, VU Main Building A-wing, 5th Floor, 1081 HV, Amsterdam, the Netherlands. Electronic address: b.kim@vu.nl.
  • Ritesh Parajuli
    Department of Medicine, University of California, Irvine, United States.
  • Rita S Mehta
    Department of Medicine, University of California, Irvine, United States.
  • Meihao Wang
    Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, PR China.
  • Min-Ying Su
    Department of Radiological Sciences, University of California, Irvine, CA 92697, USA.

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