Abstract
Activation functions play a critical role in neural networks. The paper mainly studies activation functions with four activation functions that were the selection for reference and comparison. The Mish activation function was expending as the Mish_PLUS activation function, the Sigmoid activation function, and the Tanh were combined to obtain a new Sigmoid_Tanh activation function. We used the recently popular YoLov5s and YoLov5m as the basic structure of the neural network. The function realized in this article was the recognition function of red blood cells, white blood cells, and platelets. Through the role and comparison of different activation functions in the neural network structure, the test results show that, in this paper, the training precision curve under the Sigmoid_Tanh activation function was better than that under the action of other activation functions. That means that the accuracy of cell recognition under the activation function was higher.
Highlights
The blood of an organism contains many components
On the one hand, inspired by the Mish activation function, the Mish activation function is extended based on the Mish activation function; on the other hand, the Sigmoid activation function and the Tanh activation function choose from the widely used activation functions
YoLov5s and YoLov5m as the neural network structure, and the function realized was the recognition function of white blood cells, red blood cells, and platelets
Summary
The blood of an organism contains many components. It was one of the tasks of biomedical research to find out the cells in the blood, for instance, using some methods rapidly and accurately screening of red blood cells, white blood cells, platelets, etc. The application of artificial intelligence technology in the biomedical field provided a critical research theory for the development of biomedical. Using artificial intelligence recognition technology is one of the methods to screening out different types of cells in biological blood for example, adopting machine learning and deep learning methods to screen diverse cells types [4] [5]. How to screen diverse cells quickly, efficiently, and accurately is one of the research fields of biomedicine
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