# Automatic Microscopy Analysis with Transfer Learning for Classification of Human Sperm
## 3 Results and Discussion
### Ablation Analysis  
For the human sperm morphology classification of the HuSHeM dataset, Dropout layers, and Batch Normalization layers (see Figure 4b) were employed to help the model classify the cells with higher accuracy. To investigate the influence of different components in the proposed method on the classification results, an ablation experiment was conducted. The values of the metrics obtained from the ablation experiment are shown in Table 5. When only the Dropout layers are applied in the algorithm, the performance of the model hardly improved. This indicates that the reduced prediction accuracy of the algorithm is because the model could not learn and summarize the general characteristics of the training dataset well due to the relatively concise architecture of the method. Hence, the Batch Normalization layers were added to help the model better regress the generalized features of the input training images. With the Batch Normalization layers, the classification metrics  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{**Actual**} \\ \cline{3-6}  & & **Normal** & **Tapered** & **Pyiform** & **Amorphous** \\ \hline \multirow{4}{*}{Prediction} & Normal & 99.0\% & 0.0\% & 1.7\% & 0.3\% \\  & Tapered & 0.5\% & 95.3\% & 3.2\% & 1.4\% \\  & Pyriform & 0.5\% & 4.4\% & 94.1\% & 1.1\% \\  & Amorphous & 0.0\% & 0.3\% & 1.0\% & 97.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: The confusion matrix of the prediction in the test dataset. The percentage values of correct and incorrect prediction in the table are the average of the five groups. The values in bold represent the prediction precision of each category, and the average of them is the average precision.  
Figure 6: The SCIAN dataset [12].  
grew by about 2% (the average accuracy, precision, recall, and F-score increased from 93.8% to 95.7%, from 94.2% to 96.1%, from 93.7% to 95.7%, from 93.7% to 95.6%, respectively). The Batch Normalization layer relieves the internal covariate shift phenomenon that refers to the change in the distribution of activations caused by the varied network parameters in the learning process. Therefore, the classifier with Batch Normalization layers could better capture the characteristics of the input data and improve the regression of the classification. On the basis of the upgraded learning capacity with Batch Normalization layers, the Dropout layer could further improved the classification performance slightly (the average accuracy, precision, recall, and F-score went up to 96.0%, 96.4%, 96.1%, and 96.0%, respectively). Finally, both Dropout layers and Batch Normalization layers were added to modify the AlexNet with transfer learning.  
In addition to improving the classification accuracy, the Batch Normalization layer can also speed up the training process. In experiments, two training processes with and without Batch Normalization are compared and the training curves are plotted in Figure 7. The accuracy of the network with Batch Normalization reaches its maximum value at around 400 th epochs, which is faster than that of the network without Batch Normalization (at around 1400 th epochs). The results further indicate that the Batch Normalization layers can upgrade the learning ability of the classification model.