Automatic detection of ischemic necrotic sites in small intestinal tissue using hyperspectral imaging and transfer learning.

Journal: Journal of biophotonics
PMID: 38018735

Abstract

Acquiring large amounts of hyperspectral data of small intestinal tissue with real labels in the clinic is difficult, and the data shows inter-patient variability. Building an automatic identification model using a small dataset presents a crucial challenge in obtaining a strong generalization of the model. This study aimed to explore the performance of hyperspectral imaging and transfer learning techniques in the automatic identification of normal and ischemic necrotic sites in small intestinal tissue. Hyperspectral data of small intestinal tissues were collected from eight white rabbit samples. The transfer component analysis (TCA) method was performed to transfer learning on hyperspectral data between different samples and the variability of data distribution between samples was reduced. The results showed that the TCA transfer learning method improved the accuracy of the classification model with less training data. This study provided a reliable method for single-sample modelling to detect necrotic sites in small intestinal tissue .

Authors

  • Lechao Zhang
    College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, China.
  • Jianxia Xue
    College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China.
  • Yi Xie
    Department of Plastic Surgery Peninsula Health Melbourne Victoria Australia.
  • Danfei Huang
    College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, China.
  • Zhonghao Xie
    College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China.
  • Libin Zhu
    Pediatric General Surgery, The Second Hospital of Wenzhou Medical University, Wenzhou, China.
  • Xiaoqing Chen
    College of Chemistry and Chemical Engineering, Central South University, Changsha 410083, China.
  • Guihua Cui
    Department of Chemistry, Jilin Medical College, Jilin 132013, China. cuiyuhan1981_0@sohu.com.
  • Shujat Ali
    College of Electrical and Electronic Engineering, Wenzhou University, Wenzhou, China.
  • Guangzao Huang
    Department of Automation, Xiamen University, Xiamen 361005, Fujian, China. glji@xmu.edu.cn and Interdisciplinary Program in Genetics, Texas A&M University, College Station, Texas 77843, USA.
  • Xiaojing Chen
    Department of Computer Science and Engineering, University of California, Riverside, CA, USA.

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