School of Mechanical Engineering, Xi'an Jiaotong University, Xian City 710049, China.
Shenzhen Cellauto Automation Co. Ltd., Shenzhen, China.
Contrast Media Mol Imaging. 2022 Jul 12;2022:7355233. doi: 10.1155/2022/7355233. eCollection 2022.
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.
本研究旨在解决生物实验中细胞识别的效率和准确性问题。将神经网络技术应用于细胞图像识别研究，通过构建图像识别算法来解决细胞图像识别问题。首先，深入了解计算机功能，作为一种基本的智能算法，人工神经网络（ANN）被广泛应用于解决图像识别问题。最近，反向传播神经网络（BPNN）算法已发展成为一种强大的模式识别工具，并已广泛应用于图像边缘检测。然后，详细介绍了 BPNN 的结构模型。鉴于细胞图像识别的复杂性，使用基于 ANN 和 BPNN 的算法来解决此问题。BPNN 算法具有结构简单、硬件实现容易、学习效果好等优点。接下来，设计了一种基于 BPNN 的图像识别算法，并结合边缘计算技术对图像识别过程进行优化，以提高算法识别的效率。实验结果表明，与传统的图像模式识别算法相比，设计的算法对细胞图像的识别准确率高于 93.12%，因此更有利于处理细胞图像算法。结果表明，基于 BPNN 的边缘计算可以提高细胞识别结果的科学准确性，这表明基于 BPNN 的边缘计算在细胞识别研究和应用方面具有重要的实用价值。
