A fusion model of manually extracted visual features and deep learning features for rebleeding risk stratification in peptic ulcers.

Journal: Nan fang yi ke da xue xue bao = Journal of Southern Medical University
PMID: 39819728

Abstract

OBJECTIVES: We propose a multi-feature fusion model based on manually extracted features and deep learning features from endoscopic images for grading rebleeding risk of peptic ulcers.

Authors

  • Peishan Zhou
    School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
  • Wei Yang
    Key Laboratory of Structure-Based Drug Design and Discovery (Shenyang Pharmaceutical University), Ministry of Education, School of Traditional Chinese Materia Medica, Shenyang Pharmaceutical University, Wenhua Road 103, Shenyang 110016, PR China. Electronic address: 421063202@qq.com.
  • Qingyuan Li
    Forestry and Fruit Tree Research Institute, Wuhan Academy of Agricultural Sciences, Wuhan, China.
  • Xiaofang Guo
    Department of Medical Oncology of the Eastern Hospital, the First Affiliated Hospital, Sun Yat-Sen University, Guangdong, 510700, Guangzhou, China.
  • Rong Fu
    School of Biomedical Engineering, Southern Medical University, Guangzhou 510515, China.
  • Side Liu
    Guangdong Provincial Key Laboratory of Gastroenterology, Department of Gastroenterology, Nanfang Hospital, Southern Medical University, Guangzhou, Guangdong, China; Department of Gastroenterology, Zhuhai People's Hospital (Zhuhai Clinical Medical College of Jinan University), Zhuhai, Guangdong, China. Electronic address: liuside2011@163.com.

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