## 3 Experiments and Analyses
### Result Analyses  
#### 3.3.1 Results of Ablation Experiments  
The ablation experiments are carried out among the original network and three improved network models, respectively. The network parameters are as follows: the momentum gradient is 0.9, the initial learning rate is 0.005, the weight attenuation is 0.0005, the batch size of the input image is 4 during training and each experiment is trained with 40 epochs. These networks all use the same dataset and training parameters. The original network selects Vgg16 as the backbone, while the three improved networks use ResNet50 as the backbone. The improved model 1 adds EPSA and ROI Align, the improved model 2 adds BFP on the basis of model 1 and the improved model 3, namely BDE_RCNN, adds DCN on the basis of model 2. It is decided that when the IoU between the candidate bounding box and ground truth box is larger than 0.7, the sample is considered as a positive sample. Otherwise, when it is smaller than 0.3, it is considered as a negative sample. The positive and negative samples are used to train the classification function of RPN, and then the suggested areas are accurately output for the subsequent full connection layer. Figure 4 shows the detection results of four network models, in which the green marks and blue marks denote the detection results of single red blood cells and adherent red blood cells, respectively. It is seen from the detection results of the original network that there are missed cells, as shown in the red boxes in Figure 4a. This is mainly because the extraction step of the original model is larger, resulting in inadequate learning of small features in densely distributed scenes. Compared to the original network model, the three improved models significantly improve the cell detection effects. Model 1 and model 2 still have a few missing cells, as shown by the red boxes marked in Figure 4b,c. But BDE_RCNN correctly identifies almost all single cells and adherent cells, as shown in Figure 4d. The recognition results show that the proposed BDE_RCNN has the best effect for cell detection. In order to more directly compare the recognition effect of each algorithm, the evaluation indexes of four models are listed in Table 1. A check indicates a selected model and a cross indicates an unselected model. The experimental datasets include three types: one is the dataset of single cells (SRBC), one is the dataset of adherent cells (ARBC) and the other is the mixture dataset of single cells and adherent cells (MIX). It is shown from recall, precision, mAP and F1 that BDE_RCNN is better than other models in SRBC, ARBC and MIX. In SRBC, the mAP of BDE_RCNN increases by 25.7%, 15.9% and 1.5% and F1 increases by 27.1%, 16.1% and 1.5%, respectively, compared to the original model, model 1 and model 2. In ARBC, the mAP of BDE_RCNN increases by 54.4%, 18.6% and 2.3% and F1 increases by 54.5%, 18.8% and 2.2%, respectively, compared to the other three models. In MIX, the mAP of BDE_RCNN also increases by 24.5%, 16.4% and 2.4% and F1 correspondingly increases by 26.6%, 17.2% and 3.2%, respectively. The experimental results indicate that the BDE_RCNN method performs best in multiple scenes with different datasets when it adopts BFP, DCN, EPSA and ROI Align modules, especially for adherent cells with dense distribution. The proposed method effectively solves the automatic recognition problem of red blood cells under the conditions of dense distribution, extrusion deformation, adhesion and overlap. The convergences of training loss function and the changes in model learning rate (lr) for four models are shown in Figure 5. It is shown that the first three models tend