# Automatic Microscopy Analysis with Transfer Learning for Classification of Human Sperm
## 3 Results and Discussion
### Evaluation of Performance  
The morphological classification of sperm head is a multi-classification task, so related metrics need to be defined in advance to evaluate the model better. Similar to the previous study in [29], for each class \(C_{i}\) (\(i\) is the index of corresponding class number), the evaluation metrics are given as \(TP_{i}\), \(FP_{i}\), \(FN_{i}\) and \(TN_{i}\), which denote the number of the true positive, false positive, false negative, and true negative, respectively. Additionally, the macro metrics (i.e. the average of metric of each class) is obtained to evaluate the algorithm. Figure 5: The training process of the AlexNet-based transfer learning method. The average accuracy of CE-SVM [13], APDL [14] and VGG16-based method [21] are also present for comparison. (**a**) The average accuracy varies with epochs. (**b**) The training changes loss with epochs. The average accuracy can be expressed as:  
\[AverageAccuracy=\frac{\sum_{i=1}^{l}\frac{TP_{i}+TN_{i}}{TP_{i}+FN_{i}+FP_{i}+TN_{i }}}{l} \tag{1}\]  
where \(l\) is the number of the classes, the same below. The average precision can be defined as:  
\[AveragePrecision=\frac{\sum_{i=1}^{l}\frac{TP_{i}}{TP_{i}+FP_{i}}}{l} \tag{2}\]  
The average recall can be expressed as:  
\[AverageRecall=\frac{\sum_{i=1}^{l}\frac{TP_{i}}{TP_{i}+FN_{i}}}{l} \tag{3}\]  
The average F-score can be defined as:  
\[AverageF-score=\frac{\sum_{i=1}^{l}2\times\frac{Precision_{i}\times Recall_{i}} {Precision_{i}+Recall_{i}}}{l} \tag{4}\]  
where \(Precision_{i}\) and \(Recall_{i}\) are the precision and recall of the \(i\) class respectively. Their expressions are shown in Equations (5) and (6). \[Precision_{i}=\frac{TP_{i}}{TP_{i}+FP_{i}} \tag{5}\]  
\[Recall_{i}=\frac{TP_{i}}{TP_{i}+FN_{i}} \tag{6}\]  
As summarized in Table 3, the accuracy, precision, recall, and F-Score of the proposed approach are calculated and compared with other state-of-the-art algorithms. The values shown in the table are the average of the 5 groups. It can be seen from Table 3, the transfer learning method based on AlexNet surpasses the previous methods in terms of all four metrics (i.e., average accuracy, precision, recall, and F-Score). In order to further investigate the performance of the proposed model in each category and the mutual influence between different categories, the confusion matrix was summarized and shown in Table 4. Normal sperm has the highest prediction precision (99%), while the average accuracy of tapered and pyriform sperm is relatively low (95.3% and 94.1%, respectively). The increased accuracy in the normal group is mainly attributed to the fact that the contours of normal sperm are uniform and easy to be distinguished from other species. The shape of some tapered sperms and pyriform sperms are very similar, which is likely to confuse the classifier, causing the network to misrecognize each other. The testing results also show that up to 4.4% of tapered sperm are predicted to be pyriform, while 3.2% of pyriform sperm are recognized as tapered. With the irregular profile, the amorphous sperms are also primarily differentiated from other classes. However, the large variations of amorphous sperm shape also increase the difficulty of regression in the algorithm and affect \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Methods** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline CE-SVM [13] & 78.5\% & 80.5\% & 78.5\% & 78.9\% \\ APDL [14] & 92.2\% & 93.5\% & 92.3\% & 92.9\% \\ VGG16 [21] & 94.0\% & 94.7\% & 94.1\% & 94.1\% \\ The proposed method & 96.0\% & 96.4\% & 96.1\% & 96.0\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the proposed method with the state-of-the-art algorithms. the final prediction of this category. As a result, the correctly predicted rate of amorphous is 97.2%, slightly lower than normal sperm. The proposed approach was also evaluated on SCIAN dataset.