# Application of Deep Learning to Construct Breast Cancer Diagnosis Model
## 3 ResNet101-Optimize: Adaptive Moment Estimation (ADAM)  
From the experimental results, it can be found that when using the ADAM optimizer, the average classification accuracy of 81.16% was obtained after fixing the weights of each layer in front of module 5c, 83.09% was obtained after fixing the weights of each layer in front of module 5b, and 81.16% was obtained after fixing the weights of each layer in front of module 5a (Table 7). It can be seen from Table 7 that not allowing all the deeper convolutional layers of the model to perform higher-level feature extraction on the image will improve the classification accuracy. From the perspective of ResNet101, when using the ADAM optimizer, fixing the weights of the first to the 323 rd layer can make the model develop a higher classification accuracy. \begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Prediction Category**} & \multicolumn{2}{c}{**Genuine Category**} \\ \cline{2-3}  & **B or N** & **M** \\ \hline B or N & 24 & 7 \\ M & 6 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: AlexNet classification results. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Fixed-Weight Layer**} & **Classification Accuracy 1** & **Classification Accuracy 2** & **Classification Accuracy 3** & **Average** \\ \hline Before fixing module 5c & 81.16\% & 79.71\% & 82.61\% & 81.16\% \\ Before fixing module 5b & 82.61\% & 84.06\% & 82.61\% & **83.09\%** \\ Before fixing module 5a & 79.71\% & 82.61\% & 81.16\% & 81.16\% \\ (1-311) & & & & \\ \hline \hline \end{tabular} Bold text indicates highest accuracy rate.  
\end{table} 
Table 7: The classification accuracy of different fixed layers. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**First Accuracy Rate**} & **Second Accuracy Rate** & **Third Accuracy Rate** & **Average** \\ \hline
79.71\% & 81.16\% & 81.16\% & **80.68\%** \\ \hline \hline \end{tabular} Bold text indicates highest accuracy rate.  
\end{table} 
Table 4: AlexNet classification accuracy (ADAM). \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**First Accuracy Rate**} & **Second Accuracy Rate** & **Third Accuracy Rate** & **Average** \\ \hline
81.16\% & 85.51\%, and 84.06\%, and the average classification accuracy was 83.58\% (Table 5).  
\end{table} 
Table 5: AlexNet classification accuracy (SGDM). ResNet101-Optimizer: Stochastic Gradient Descent with Momentum (SGDM)  
From the experimental results, it was observed that the average classification accuracy of 79.71% was obtained after fixing the weights of each layer in front of module 5c, 82.61% was obtained after fixing the weights of each layer in front of module 5b, and 79.71% was obtained after fixing the weights of each layer in front of module 5a (Table 8). From Table 8, it can be observed that when ResNet101 uses SGDM as the optimizer, fixing the weights of layer one to layer 323 still results in a better average classification accuracy rate of 82.61%. Although, the performance is not as good as when using ADAM (83.09%). Generally, the classification accuracy rate when using ADAM optimizer is still slightly better than when using SGDM. After applying the data to ResNet101 with ADAM as the optimizer, the classification accuracy of 85.51% was obtained, and the results were; 25 of the benign or normal cases were correctly classified and five were misclassified as malignant; 34 of the malignant cases were correctly classified and five were misclassified as benign or normal (Table 9). From the experimental results, it can be observed that when using ADAM as the optimizer, the average classification accuracy of 85.51% was obtained after fixing the weight of each layer before the merge point mixed10. The average classification accuracy of 87.44% was obtained after setting the weights of each layer before the merge point mixed9; the average classification accuracy of 90.71% was obtained after setting the weights of each layer before the merge point mixed8 (Table 10).