Attention mechanism CNN-based 5-day and 9-day incubated egg embryo image classification method
A classification method and attention technology, applied to computer components, character and pattern recognition, instruments, etc., can solve the problems of misjudgment of weak embryos, affecting accuracy, misjudgment of weak embryos as live embryos, etc., to achieve enhanced efficiency, The effect of strong stability and enhanced important features
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[0022] The present invention will be described in further detail below with reference to specific embodiments.
[0023] The flow chart of the present invention is as follows figure 1 As shown, 2500 images of blood vessels of egg embryos on day 5 and 10,000 images of blood vessels of embryos on day 9 were first used, and the ratio of positive and negative samples (dead and live embryos) in the 5-day and 9-day datasets was 1:1, and the Use 0 and 1 as labels to construct a dataset; then use the residual module to stack into a backbone network, apply the SENet module to generate a channel-based attention mechanism feature map, and then follow the channel separation convolution to fully extract the features of each channel, and then Atrous pyramid convolution is used to extract multi-scale semantic information, and a strong semantic attention feature saliency map is generated as a weight mask, which is weighted with the original feature map as an attention mechanism module, and ins...
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