ArticleContrast media & molecular imaging2022
Cell Recognition Using BP Neural Network Edge Computing.
Article in Contrast media & molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- UWB Indoor Localization Based on Artificial Rabbit Optimization Algorithm and BP Neural Network.Biomimetics (Basel, Switzerland) · 2025Article
- The interpretable machine learning model for depression associated with heavy metals via EMR mining method.Scientific reports · 2025Article
- Assessment of EMR ML Mining Methods for Measuring Association between Metal Mixture and Mortality for Hypertension.High blood pressure & cardiovascular prevention : the official journal of the Italian Society of Hypertension · 2024Article
- The interpretable machine learning model associated with metal mixtures to identify hypertension via EMR mining method.Journal of clinical hypertension (Greenwich, Conn.) · 2024Article
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Authors and funding
3 authors.
Funding
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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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Registered trials
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