Deep learning with a convolutional neural network model to differentiate renal parenchymal tumors: a preliminary study.

Journal: Abdominal radiology (New York)
Published Date: Mar 3, 2021

Abstract

PURPOSE: With advancements in medical imaging, more renal tumors are detected early, but it remains a challenge for radiologists to accurately distinguish subtypes of renal parenchymal tumors. We aimed to establish a novel deep convolutional neural network (CNN) model and investigate its effect on identifying subtypes of renal parenchymal tumors in T2-weighted fat saturation sequence magnetic resonance (MR) images.

Authors

  • Yao Zheng
    School of Pharmaceutical Science, South-Central University for Nationalities, Wuhan, Hubei, China.
  • Shuai Wang
    Department of Intensive Care Unit, China-Japan Union Hospital of Jilin University, Changchun, China.
  • Yan Chen
    Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
  • Hui-Qian Du
    School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China. duhuiqian@bit.edu.cn.

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