## 5 Conclusions and Future Work  
The analysis and research of EMs are essential. Therefore, a suitable method for EM detection needs to be explored. After summarizing and analyzing the work related to EM detection, we designed the SEM-RCNN for the detection of EMs. In terms of applications, to fully demonstrate the feasibility of SEM-RCNN for EM detection, model  
Figure 17: The results of SEM-RCNN for cell dataset detection.The color boxes indicate the positions predicted by SEM-RCNN, for which its captions indicate the predicted categories. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Evaluation Metrics** & **IoU** & **mAP** & **Precision** & **Recall** & **F1-Score** \\ \hline SEM-RCNN & 0.905 & 0.907 & 0.898 & 0.910 & 0.904 \\ Mask RCNN & 0.875 & 0.850 & 0.843 & 0.853 & 0.848 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Detection result of SEM-RCNN for blood cell detection. training and testing were conducted in a small dataset of EMs and a large dataset of blood cells, respectively. The final detection results demonstrate the feasibility of SEM-RCNN for detecting EMs. In terms of technology, an improved method combining Mask RCNN with SENet is proposed in this paper. To verify the feasibility of the improved method, the detection results of SEM-RCNN and the original Mask RCNN are compared on the EMDS-6 dataset and blood cell dataset, respectively. The comparison results showed that the detection results of SEM-RCNN improved two to three points in mAP than that of the original Mask RCNN. Finally, SEM-RCNN achieved a 0.511 mAP on EMDS-6 and 0.907 mAP on the blood cell dataset. This paper fills the gap in computer-aided multi-class EM detection research. However, considering the continuous innovation of related technologies and the challenges that need to be faced in practical applications, there is still more research potential and room for improvement in several aspects of this study. Regarding current research results, further research content with respect to our work will mainly be considered from EMs data. From the detection results on dataset EMDS-6 and blood cell dataset, it can be seen that sufficient training data can significantly improve the detection effect of the model. In contrast, insufficient training data can lead to poor detection effects from the model. Therefore, in the follow-up study, we will focus part of our efforts on expanding the existing microbial dataset to build an EMs dataset with more sufficient data and to better meet the training of detection models. [PERSON]. ([PERSON]), Methodology, validation, data curation, writing--original draft preparation, review and editing, and visualization; P.M., methodology, software, validation, formal analysis, data curation, writing--original draft preparation, and visualization; T.J., investigation; funding acquisition; X.Z., resources; W.T., resources; [PERSON]. ([PERSON]), validation; S.Z., validation; X.H., validation; M.G., investigation; C.L., conceptualization, methodology, data curation, writing--original draft preparation, writing--review and editing, supervision, and project administration. All authors have read and agreed to the published version of the manuscript. This work is supported by the \"Natural Science Foundation of China\" (No. 61806047 and 61971118) and \"Sichuan Science and Technology Program\" (No. 2021 YFH0069, 2021 YFQ0057, and1614 2022 YFS0565). EMDS-6 is at: [[https://figshare.com/articles/dataset/EMDS-6/171250](https://figshare.com/articles/dataset/EMDS-6/171250)]([https://figshare.com/articles/dataset/EMDS-6/171250](https://figshare.com/articles/dataset/EMDS-6/171250)) 25/1 (accessed on 15 February 2022). We thank [PERSON] and [PERSON] for their important discussions. We also thank [PERSON], due to his contribution is considered as important as the first author in this paper. There is no conflict of interest in this paper. The following abbreviations are used in this manuscript: