# A Novel Architecture to Classify Histopathology Images Using Convolutional Neural Networks
## 5 Discussion
### Comparison between Different State-of-the-Art CNN  
[PERSON] et al. [22] presented a network with three convolution layers, where the number of kernels is 64, 32, and 16, respectively. The kernels size used was \(5\times 5,3\times 3\), and \(3\times 3\), respectively. Every convolution layer was followed by a batch normalization layer and max-pooling layer, except for the third convolution layer, which was followed by a batch normalization layer only. There are two dropout layers, one after the second convolution layer and the second before the fully connected layer. Compared to our network, [PERSON] architecture was the second-best architecture, among the set of competitors, for both RMSprop and Adam optimizers. The authors placed the normalization layer before the activation layer and the dropout layer in the middle of the convolution blocks. The main difference between our proposed architecture and the architecture proposed by [PERSON] et al. [22] relies on the number of convolution layers used. From the results obtained, it seems that the choice of the number of convolution layers plays a fundamental role in the performance of the model. In particular, a network with just three convolution layers seems to be not sufficient for dealing with the complexity of the application at hand. The architecture of [PERSON] et al. [22] is shown in Figure 7.  
[PERSON] et al. [24] introduced a network with six convolution layers and no fully connected layers. This network achieved the best results, among the competitors of our proposed architecture, for both the optimizers. The kernel size used in both the convolution layers and the pooling layers is the highest compared to all the tested CNNs. No dropout layers or batch normalization layers were used. The results of [PERSON] and RMSProp optimizers are comparable. The differences between this network and our network are the size of the kernels used (which is higher than our proposed network), the absence of any regularization layers, the absence of any fully connected layers, and the number of convolutional layers, (which is higher in our model). The main difference in terms of design choices between the architecture of [PERSON] et al. [24] and our architecture is the absence of any regularization layers from their architecture. According to the experiments we performed, this seems to be the main cause for the lower performance of the network proposed by [PERSON] and coauthors. Figure 4 shows the architecture of [PERSON] et al. [24]. [PERSON] et al. [20] presented a network with five convolution layers, one fully connected layer, three dropout layers, and six normalization layers. The authors placed the normalization layer after the activation layer, and the dropout layer in the middle of the convolution blocks. This is the only network with an activation function other than the ReLU function. The results of [PERSON] and RMSProp  
Figure 4: The architecture of [PERSON] et al. [24]. optimizers are different, with [PERSON] being the best optimizer. Compared to our proposed network, the main differences are the number of convolution layers, the presence of the normalization layer after the activation function, and the usage of only one fully connected layer. The difference between the performance of our proposed architecture and the architecture presented by [PERSON] et al. [20] can be explained by the number of convolution layers (that is lower with respect to our architecture) and by the position of the normalization layer after the activation layer. In particular, placing the normalization layer after the activation function was not suggested in the original paper that introduced the normalization layer [38]. Figure 5 shows the architecture of [PERSON] et al. [20]. [PERSON] et al. [25] designed an architecture with 4 convolution layers and two fully connected layers. The authors also placed the normalization layer after the activation layer. Moreover, the authors placed a normalization layer in the classifier block.