Gastrointestinal endoscopic image classification and early cancer detection system based on multi-task neural network
A neural network and digestive tract technology, applied in the field of digestive tract endoscopic image classification and early cancer detection system based on multi-task neural network, can solve problems such as single detection or identification, reduce morbidity and mortality, and reduce human factors , the effect of improving the efficiency of diagnosis
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[0039] The embodiments of the present invention will be described in detail below, but the protection scope of the present invention is not limited to the examples.
[0040] use figure 1 Using the network structure in , 1332 abnormal images and 1096 normal images are used to train a multi-task neural network to obtain an automatic classification and detection model.
[0041] The specific implementation method is:
[0042] (1) Before training, initialize the network parameters with the pre-trained VGG-16 model, and adjust the images in the training set to a uniform size of 300×300;
[0043] (2) During training, the image is randomly cropped to 224×224, and the mean value is subtracted. Set the initial learning rate to 0.0001, the decay rate to 0.9, and decay once every two cycles. Minimize the loss function using mini-batch stochastic gradient descent. The batch size is set to 12. In order to prevent overfitting, randomly kill some neurons in the fully connected layer in t...
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