A deep learning-machine learning fusion approach for the classification of benign, malignant, and intermediate bone tumors.

Journal: European radiology
Published Date:

Abstract

OBJECTIVES: To build and validate deep learning and machine learning fusion models to classify benign, malignant, and intermediate bone tumors based on patient clinical characteristics and conventional radiographs of the lesion.

Authors

  • Renyi Liu
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China.
  • Derun Pan
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong Province, China.
  • Yuan Xu
    Key Laboratory of Resource Biology and Biotechnology in Western China, Ministry of Education, College of Life Sciences, Northwest University, Xi'an, China.
  • Hui Zeng
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, Guangdong, China.
  • Zilong He
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Jiongbin Lin
    Department of Radiology, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, People's Republic of China.
  • Weixiong Zeng
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Zeqi Wu
    Department of Radiology, Nanfang Hospital, Southern Medical University, 1838 Guangzhou Avenue North, Baiyun District, Guangzhou, Guangdong Province, People's Republic of China.
  • Zhendong Luo
    Department of Radiology, University of Hong Kong-Shenzhen Hospital, Shenzhen, Guangdong Province, China.
  • Genggeng Qin
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou, China.
  • Weiguo Chen
    Department of Radiology, Nanfang Hospital, Southern Medical University, Guangzhou 510515, China. Electronic address: chenweiguo1964@21cn.com.