Neural network models with excellent performance require excellent training datasets. Because of the invariance of translation, rotation, scaling and other forms, it is widely used in image recognition. CNN achieves dimensionality reduction by designing convolution kernels and pooling kernels, so they can process complex data with limited resources. CNN is comprised of a convolutional layer, pooling layer, fully connected layer, and activation function. This artificial neural network possesses multiple hidden layers and is based on research on cells of the cat’s visual cortex. Convolutional neural network (CNN) is the most widely used neural network in image recognition. In the intelligent medical diagnosis system, experts not only have confidence in features, but also in visual images, which improves diagnoses’ quality. Characterization and evaluation of visual images introduces significant challenges to the doctor’s evaluation. With the development of artificial intelligence, neural networks have featured prominently in image recognition and have potential pioneering applications in diagnosis of breast cancer. Breast cancer is one of the most common cancers worldwide.
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