# A Novel Architecture to Classify Histopathology Images Using Convolutional Neural Networks
## 4 Results
### Results
#### 4.2.2 The Results of Different Designs  
Four different designs were tested to check the performance of the network. The first design considers the network with two dropout layers in the classifier block, and the normalization layer before the activation layer. The second design is similar to the first design but without any dropout layers. The third design places the normalization layer after the activation layer and adds dropout layers in the classifier block. The fourth design is similar to the third design but without adding any dropout layers in the classifier block. Tables 4 and 5 show the results for both the Adam and RMSProp optimizers. The ReLU activation function has the lowest score compared to other activation functions across the four designs. The best performance of ReLU (with Adam optimizer) was obtained in the third design, followed by the fourth design. The worst score was by using the second design. The results with the RMSprop optimizer were poorer than the Adam optimizer. The best result was achieved with the second design, followed by the third design. The lowest result was obtained with the first design. Overall, the best result achieved by ReLU was 91.78%. The LeakyReLU activation function had slightly lower accuracy than the ReLU function. With Adam optimizer, the third design was the best performer, followed by the fourth design. The worse result was obtained with the second design. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Tanh** & **ReLU** & **LeakyReLU** & **ELU** & **SELU** \\ \hline \(H1\) & 91.70\% & 87.68\% & 87.37\% & 93.66\% & 92.73\% \\ \hline \(H2\) & 92.96\% & 85.45\% & 87.17\% & 91.76\% & 92.82\% \\ \hline \(H3\) & 94.39\% & 91.78\% & 89.96\% & 94.40\% & 90.16\% \\ \hline \(H4\) & 95.46\% & 89.33\% & 89.11\% & 93.85\% & 92.71\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The AUC results of the Adam optimizer. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **ReLU** & **LeakyReLU** & **ELU** & **SELU** & **Sigmoid** & **Tanh** \\ \hline \(Adam\) & 87.68\% & 87.37\% & 93.66\% & 92.73\% & 84.03\% & 91.70\% \\ \hline \(RMSprop\) & 85.01\% & 83.02\% & 92.99\% & 88.43\% & 84.49\% & 92.00\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The AUC results of both optimizers for the different activation functions. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Tanh** & **ReLU** & **LeakyReLU** & **ELU** & **SELU** \\ \hline \(H1\) & 92.00\% & 85.01\% & 83.02\% & 92.99\% & 88.43\% \\ \hline \(H2\) & 94.09\% & 89.77\% & 90.37\% & 87.62\% & 94.25\% \\ \hline \(H3\) & 93.35\% & 88.40\% & 90.38\% & 92.26\% & 93.86\% \\ \hline \(H4\) & 93.43\% & 87.70\% & 90.11\% & 94.86\% & 91.58\% \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The AUC results of the RMSProp optimizer. With the RMSprop optimizer, the best performance was obtained in the third design, followed by the second design. The worst performance was achieved by using the first design. The results of Adam and RMSprop optimizers were significantly different, especially for the first design. Overall, the best result achieved by LeakyReLU was 90.38%. The ELU activation function had higher results compared to both ReLU and LeakyReLU. With Adam optimizer, the best performance was achieved by using the third design, followed by the fourth design. The lowest performance was achieved with the second design. With the RMSProp optimizer, the best result was achieved by using the fourth design, followed by the first design. The worst result was achieved with the second design. The results of both Adam and RMSprop optimizers were comparable, except for the second design. Overall, the best result achieved by the ELU activation function was 94.86%. The SELU activation function had slightly lower performance than the ELU activation function. With Adam optimizer, the best performance was achieved by using the second design, followed by the first design. The lowest performance was obtained with the third design.