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. 2022 Jul 12:2022:7355233.
doi: 10.1155/2022/7355233. eCollection 2022.

Cell Recognition Using BP Neural Network Edge Computing

Xiangxi Du  1   2   3 , Muyun Liu  2   3 , Yanhua Sun  1
Affiliations

Cell Recognition Using BP Neural Network Edge Computing

Xiangxi Du et al. Contrast Media Mol Imaging. .

Abstract

This exploration is to solve the efficiency and accuracy of cell recognition in biological experiments. Neural network technology is applied to the research of cell image recognition. The cell image recognition problem is solved by constructing an image recognition algorithm. First, with an in-depth understanding of computer functions, as a basic intelligent algorithm, the artificial neural network (ANN) is widely used to solve the problem of image recognition. Recently, the backpropagation neural network (BPNN) algorithm has developed into a powerful pattern recognition tool and has been widely used in image edge detection. Then, the structural model of BPNN is introduced in detail. Given the complexity of cell image recognition, an algorithm based on the ANN and BPNN is used to solve this problem. The BPNN algorithm has multiple advantages, such as simple structure, easy hardware implementation, and good learning effect. Next, an image recognition algorithm based on the BPNN is designed and the image recognition process is optimized in combination with edge computing technology to improve the efficiency of algorithm recognition. The experimental results show that compared with the traditional image pattern recognition algorithm, the recognition accuracy of the designed algorithm for cell images is higher than 93.12%, so it has more advantages for processing the cell image algorithm. The results show that the BPNN edge computing can improve the scientific accuracy of cell recognition results, suggesting that edge computing based on the BPNN has a significant practical value for the research and application of cell recognition.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Neuron structure model.
Figure 2
Figure 2
BPNN model.
Figure 3
Figure 3
Image edge classification: (a) step edge and (b) roof edge.
Figure 4
Figure 4
(a) Cell original gray image. (b) Sobel operator segmentation effect. (c) Laplacian operator segmentation effect.
Figure 5
Figure 5
Cell recognition training results (S1 = 15). The horizontal line is the expected error value.
See this image and copyright information in PMC

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