Cell recognition based on atomic force microscopy and modified residual neural network.

Journal: Journal of structural biology
PMID: 37451561

Abstract

Cell recognition methods are in high demand in cell biology and medicine, and the method based on atomic force microscopy (AFM) shows a great value in application. The difference in mechanical properties or morphology of cells has been frequently used to detect whether cells are cancerous, but this detection method cannot be a general means for cancer cell detection, and the traditional artificial feature extraction method also has its limitations. In this work, we proposed an analytic method based on the physical properties of cells and deep learning method for recognizing cell types. The residual neural network used for recognition was modified by multi-scale convolutional fusion, attention mechanism and depthwise separable convolution, so as to optimize feature extraction and reduce operation costs. In the method, the collected cells were imaged by AFM, and the processed images were analyzed by the optimized convolutional neural network. The recognition results of two groups of cells (HL-7702 and SMMC-7721, SGC-7901 and GES-1) by this method show that the recognition rate of dataset with the combination of cell surface morphology, adhesion and Young's modulus is higher, and the recognition rate of the dataset with optimal resolution is higher. Our study indicated that the recognition of physical properties of cells using deep learning technology can serve as a universal and effective method for the automated analysis of cell information.

Authors

  • Junxi Wang
    International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China; Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022, China; Zhongshan Institute of Changchun University of Science and Technology, Zhongshan, China.
  • Mingyan Gao
    College of Computer and Information Science, Southwest University, Chongqing 400715, China.
  • Lixin Yang
    International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China; Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022, China.
  • Yuxi Huang
    International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China; Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022, China.
  • Jiahe Wang
    International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China; Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022, China.
  • Bowei Wang
    International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun 130022, China; Ministry of Education Key Laboratory for Cross-Scale Micro and Nano Manufacturing, Changchun University of Science and Technology, Changchun 130022, China; Zhongshan Institute of Changchun University of Science and Technology, Zhongshan, China.
  • Guicai Song
    College of Physics, Changchun University of Science and Technology, Changchun 130022, China. Electronic address: songcust@163.com.
  • Zuobin Wang
    International Research Centre for Nano Handling and Manufacturing of China, Changchun University of Science and Technology, Changchun, 130022, China; JR3CN & IRAC, University of Bedfordshire, Luton, LU1 3JU, UK.

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