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Classification of gastric cancerous tissues by a residual network based on optical coherence tomography images

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Abstract

Optical coherence tomography (OCT) is a noninvasive, radiation-free, and high-resolution imaging technology. The intraoperative classification of normal and cancerous tissue is critical for surgeons to guide surgical operations. Accurate classification of gastric cancerous OCT images is beneficial to improve the effect of surgical treatment based on the deep learning method. The OCT system was used to collect images of cancerous tissues removed from patients. An intelligent classification method of gastric cancerous tissues based on the residual network is proposed in this study and optimized with the ResNet18 model. Four residual blocks are used to reset the model structure of ResNet18 and reduce the number of network layers to identify cancerous tissues. The model performance of different residual networks is evaluated by accuracy, precision, recall, specificity, F1 value, ROC curve, and model parameters. The classification accuracies of the proposed method and ResNet18 both reach 99.90%. Also, the model parameters of the proposed method are 44% of ResNet18, which occupies fewer system resources and is more efficient. In this study, the proposed deep learning method was used to automatically recognize OCT images of gastric cancerous tissue. This artificial intelligence method could help promote the clinical application of gastric cancerous tissue classification in the future.

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Funding

This research was funded by National Natural Science Foundation of China (No. 82172112, No. 81901907, No. 62105098, No. U20A20388), Beijing Institute of Technology Research Fund Program for Young Scholars, the Fundamental Research Funds for the Central Universities (No. 2021CX11018), and National Key Research and Development Program of China (No. 2020YFC2007300, No. 2020YFC2007301).

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Author notes

  1. Site Luo and Yuchen Ran contributed equally to this work.

Authors and Affiliations

  1. Key Laboratory for Micro/Nano Optoelectronic Devices of Ministry of Education & Hunan Provincial Key Laboratory of Low-Dimensional Structural Physics and Devices, School of Physics and Electronics, Hunan University, Changsha, 410082, China

    Site Luo & Huihui Huang

  2. School of Life Science, Beijing Institute of Technology, Beijing, 100081, China

    Yuchen Ran, Lifei Liu & Xiaoying Tang

  3. Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, 100081, China

    Xiaoying Tang & Yingwei Fan

Authors
  1. Site Luo
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  2. Yuchen Ran
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  3. Lifei Liu
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  4. Huihui Huang
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  5. Xiaoying Tang
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  6. Yingwei Fan
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Corresponding author

Correspondence to Yingwei Fan.

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All procedures followed were in accordance with the ethical standards of the responsible committee on human experimentation. Informed consent was obtained from all patients enrolled in the study.

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The authors declare no competing interests.

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Luo, S., Ran, Y., Liu, L. et al. Classification of gastric cancerous tissues by a residual network based on optical coherence tomography images. Lasers Med Sci 37, 2727–2735 (2022). https://doi.org/10.1007/s10103-022-03546-8

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  • DOI: https://doi.org/10.1007/s10103-022-03546-8

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