CPC G06T 7/0012 (2013.01) [G06N 3/045 (2023.01); G06T 7/90 (2017.01); G06V 10/82 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30041 (2013.01)] | 19 Claims |
1. A method for training recognition model, comprising:
obtaining a color fundus image sample associated with a label value, inputting the color fundus image sample into a preset recognition model containing initial parameters, wherein the preset recognition model comprises an input unit, a first convolutional neural network, and a second convolutional neural network sequentially connected together;
extracting a red channel image in a red channel from the color fundus image sample in the input unit;
inputting the red channel image into the first convolutional neural network to obtain a first recognition result and a feature image of the red channel image;
combining the color fundus image sample with the feature image to generate a combined image, and inputting the combined image into the second convolutional neural network to obtain a second recognition result;
inputting the label value, the first recognition result, and the second recognition result into a preset loss function to obtain a total loss value; wherein the loss function comprises a first loss weight of the first convolutional neural network and a second loss weight of the second convolutional neural network;
when the total loss value is less than or equal to a preset loss threshold, ending the training of the preset recognition model.
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