# Automatic Microscopy Analysis with Transfer Learning for Classification of Human Sperm
## 4 Conclusions  
In this research, a deep Convolutional Neural Network using AlexNet structure was modified by adding Batch Normalization layers to classify sperm automatically. The proposed approach was trained and tested on the HuSHeM dataset, a publicly available sperm dataset that contains four distinct categories. Cross-validation was conducted in the experiment to evaluate the performance. The experiment results show that our method outperforms the state-of-the-art algorithms in the given metrics: classification accuracy (96.0% vs. 94.0%), average precision (96.4% vs. 94.7%), average recall (96.1% vs. 94.1%) and average F-score (96.0% vs. 94.1%).  
Compared with the method using VGG structure, the proposed method applies the pre-training parameters without fine turning, which could accelerate the training process and save computing resources.Although the more advanced deep learning architectures usually achieve better performance when facing with complex scenarios (such as the ImageNet dataset), a concise network architecture might perform better if the dataset is relatively small and the feature information is not very diverse. The comparison results confirm the simple network structure is able to extract the feature effectively without overfitting. It is also worth mentioning that Batch Normalization in the proposed method can not only accelerate the learning of the network, but also improve the final sperm classification results on the HuSHeM dataset. In addition, data preprocessing can improve algorithm performance by improving data quality. In a nutshell, this research applies the transfer learning technique and presents a new deep learning model for sperm classification. The improved classification performance with reduced computational burden enables to fully automate the semen analysis in IVF applications.  
Conceptualization, R.L. and J.L.; methodology, R.L., J.Y. and J.L.; software, R.L. and Y.Y.; validation, R.L., M.W.([PERSON]) and J.L.; formal analysis, R.L. and M.W.([PERSON]); investigation, R.L.; resources, J.L.; data curation, M.W.([PERSON]) and R.L.; writing--original draft preparation, R.L. and M.W.([PERSON]); writing--review and editing, J.Y. and J.L.; visualization, M.W.([PERSON]); supervision, J.L. and J.Y.; project administration, J.L.; funding acquisition, J.L. All authors have read and agreed to the published version of the manuscript.  
\begin{table}
\begin{tabular}{l c c c c} \hline
**Classifiers** & **CDNN** & **SVM** & **Random Forest** & **Proposed Method** \\ \hline Accuracy & 92.8\% & 92.3\% & 93.9\% & 96.0\% \\ \hline \end{tabular}
\end{table}
Table 7: The performance of different classifiers in the sperm classification task of the HuSHeM dataset.  
**Funding:** This work was supported in part by the Guangdong Province Basic and Applied Basic Research Fund Project 2019A1515110175, in part by the Research Grants Council (RGC) of Hong Kong under Grant 21212720.  
**Data Availability Statement:** The HuSHeM dataset is available at [[https://data.mendeley.com/datasets/tt3](https://data.mendeley.com/datasets/tt3) yj2 pf38/1]([https://data.mendeley.com/datasets/tt3](https://data.mendeley.com/datasets/tt3) yj2 pf38/1) (accessed on 24 June 2017).  
**Conflicts of Interest:** The authors declare no conflict of interest.