Dynamic Learning Rate in Deep CNN Model for Metastasis Detection and Classification of Histopathology Images

Figure 4

General architecture of CNN. The first convolutional layer extracts features from the input image with dimension with channels. The pooling layer performs dimensionality reduction, and the data is converted to a one-dimensional array by the flattening layer. The fully connected layer generates the output after classification.