HIGHLIGHTS
SUMMARY
Convolutional neural_networks (CNN) for instance have been demonstrated to have good prediction performances in recognizing simple patterns in data, which are then used to form more complex patterns within deeper layers. Convo‐ lutional neural_networks (CNNs) are the most widely used architectures in deep learning approaches that are generally composed of an input layer, several hidden layers (convo‐ lution, pooling, and fully connected "dense" layers), and an output layer. The deep neural_network developed for brain tumor classification was based on a one‐dimensional CNN (1D‐CNN) characterized by the fact that during convolution the CNN . . .
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