Figure 6

Schematic diagram of the CNN fine-tuning considered for identifying bladder cancer. Each pre-trained CNN was fine-tuned by appending two batch normalization layers, a global average pooling layer, dropout layers with the probability of 50%, a dense layer to improve cancer identification, and Softmax activation layer. The last layer delivers different label probabilities for each input image with respect to each classification task.