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

• Published in: Journal of biophotonics Vol. 17; no. 2; pp. e202300315 - n/a
• Main Authors: Zhang, Lechao, Xue, Jianxia, Xie, Yi, Huang, Danfei, Xie, Zhonghao, Zhu, Libin, Chen, Xiaoqing, Cui, Guihua, Ali, Shujat, Huang, Guangzao, Chen, Xiaojing
• Format: Journal Article
• Language: English
• Published: Weinheim WILEY‐VCH Verlag GmbH & Co. KGaA 01.02.2024; Wiley Subscription Services, Inc
• Subjects: Animals; assisted diagnosis; Humans; Hyperspectral Imaging; Ischemia; Learning; Machine Learning; Rabbits; Small intestine; small intestine tissue; Tissues; transfer component analysis; Transfer learning; small intestine tissue; transfer component analysis; transfer learning; hyperspectral imaging; assisted diagnosis
• ISSN: 1864-063X; 1864-0648; 1864-0648
• DOI: 10.1002/jbio.202300315

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 . Ischemic necrosis of small intestinal tissue is a serious gastrointestinal disorder. We used hyperspectral imaging to acquire hyperspectral data of small intestinal tissue. Reducing variability between different samples of data using TCA method. It allows the model to better distinguish between normal and necrotic areas of small intestinal tissue.