## 4 Experiment Results and Analysis
### Extensive Experiment  
To compare the detection performance of SEM-RCNN with existing deep learning-based detectors, we chose several classical detectors for our experiments. Table 4 introduces the detection results and population variances (with 5-fold cross validation) of some classical deep-learning-based detectors.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**Ours**} & \multirow{2}{*}{**SSD**} & \multicolumn{2}{c}{**Faster**} & \multirow{2}{*}{**RetinaNet**} & \multirow{2}{*}{**YOLOv3**} & \multirow{2}{*}{**YOLOv4**} \\  & & & & **R-CNN** & & & \\ \hline mAP & 0.511 & 0.421 & 0.377 & 0.401 & 0.425 & 0.436 \\ Varp & \(1.46\times 10^{-5}\) & \(6.64\times 10^{-6}\) & \(1.38\times 10^{-5}\) & \(8.46\times 10^{-5}\) & \(3.78\times 10^{-5}\) & \(5.65\times 10^{-5}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Detection result of some classical deep-learning-based detectors (with 5-fold cross validation).  
Figure 16: The example images of _Stentor_ which are classified as _Paramecium_ in EMDS-6.The color boxes indicate the positions predicted by SEM-RCNN, for which its captions indicate the predicted categories and the probabilities.  
Figure 15: The example images of _Codosiga_ and _Epistylis_ in EMDS-6.  
From the final detection results, SEM-RCNN achieves better detection performance than SSD, Faster R-CNN, RetinaNet, YOLOv3, and YOLOv4. The results prove that it is feasible to improve the detection effectiveness of a detector by increasing its feature extraction capability.  
To further prove the object detection performance of the proposed SEM-RCNN model, another dataset of EMs should be applied for the extensive experiment. However, by reviewing our previous works about EMs image analysis and EMs dataset [15; 18; 19; 20; 21; 22; 24; 26; 28; 29; 95], there are few proper open-access EMs dataset, which is caused by several objective reasons, including the uneven natural lighting and too many impurities while imaging. Hence, a dataset containing 8 types of blood cells is applied for the extensive experiment, including _erythrocytes, basophils, eosinophils, lymphocytes, monocytes, neutrophils, platelets, and immunoglobulins_. The reasons why we choose the blood cell images consisting four aspects: firstly, the images of EMs and blood cells are both microscopic images, which have strong morphological and shape similarities; secondly, both of them are non-directional images, where all objects in these images have no fixed positive direction; thirdly, both of them can be applied for multi-class detection tasks; finally, both of them have lots of noise and redundant impurities. Besides, the application of the blood cell image dataset can prove the strong generalization ability of the proposed SEM-RCNN. There are 17,090 labeled blood cell images in total, and the proposed SEM-RCNN is trained for 150 epochs with finetuning based on the pre-trained model for MS-COCO dataset. The result is shown in Table 5. By reviewing Table 5, we find that the proposed SEM-RCNN can achieve excellent blood cell detection performance. Most of the evaluation metrics are more than 0.9, which performs better than Mask RCNN significantly. The detection result is shown in Figure 17, which shows satisfactory detection performance of the proposed SEM-RCNN.