# Complete Blood Cell Detection and Counting Based on Deep Neural Networks
## 5 Conclusions  
In this paper, we propose a novel CNN-based blood cell detection and counting architecture. In this architecture, we adopt VGG-16 as the backbone. The feature maps generated by VGG-16 are enriched by feature fusion and block attention mechanism (CBAM). The concepts of RPN and RoI Pooling from Faster R-CNN are used for blood cell detection. We used the BCCD dataset of blood smear images for the performance evaluation of the proposed architecture. The original images were preprocessed, including image augmentation, enlargement, sharpening, and blurring. Five models with different settings were constructed. The experiments on the RBCs, WBCs, and platelets detection were performed under two confidence scores: 0.9 and 0.8, respectively. For RBC detection, Model 3, which enlarges input images 1.5-fold and uses images in both RGB and grayscale color spaces, achieved the best recalls: 82.3% and 86.7% under two confidence scores: 0.9 and 0.8, respectively. Meanwhile, it achieved a precision of 74.7% and 70.1% under the two confidence scores. Model 5, which uses RBG images only, scores with an impressive precision; however, it also scores notorious recalls, especially when RBCs are heavily overlapping. WBC detection is usually the easiest task in blood cell counting due to the size and peculiar color of WBCs. For WBC detection, Model 1, which performs image preprocessing and uses both RBG and grayscale images, outperforms other models. The precision and recall are 76.1% and 95%, respectively, under a confidence score of 0.9, and 69.1% and 96.4%, respectively, under a confidence score of 0.8. Platelets are smaller and fewer than RBCs and WBCs. Platelet detection is harder than RBC and WBC detections, especially when the platelets are clustered. \begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & & \multicolumn{6}{c}{**Confidence Score: 0.9**} & \multicolumn{6}{c}{**Confidence Score: 0.8**} \\ \hline
**Index * & \(P_{\text{Fig10}}\) & \(R_{\text{Fig10}}\) & \(P_{\text{Fig11}}\) & \(R_{\text{Fig11}}\) & \(P_{\text{v}}\) & \(R_{\text{v}}\) & \(P_{\text{Fig10}}\) & \(R_{\text{Fig10}}\) & \(P_{\text{Fig11}}\) & \(R_{\text{Fig11}}\) & \(P_{\text{v}}\) & \(R_{\text{v}}\) \\ \hline Model 1 & 1 & 0.5 & 1 & 0.25 & 0.498 & 0.412 & 1 & 0.5 & 1 & 0.25 & **0.508** & 0.53 \\ Model 2 & 1 & 1 & 1 & 0.25 & **0.529** & **0.429** & 1 & 1 & 0.5 & 0.25 & 0.47 & **0.67** \\ Model 3 & 0 & 0 & 0 & 0 & 0.382 & 0.288 & 1 & 0.5 & 0 & 0 & 0.453 & 0.576 \\ Model 4 & 1 & 1 & 0 & 0 & 0.381 & 0.431 & 1 & 1 & 0.333 & 0.25 & 0.351 & 0.518 \\ Model 5 & 0 & 0 & 0 & 0 & 0.114 & 0.067 & 0 & 0 & 0 & 0 & 0.14 & 0.116 \\ \hline \hline \end{tabular}  
* \(P_{\text{Fig10}}\) and \(R_{\text{Fig10}}\) refer to the precision and recall of platelet detection on Figure 10, \(P_{\text{Fig11}}\) and \(R_{\text{Fig11}}\) refer to those on Figure 11, and \(P_{v}\) and \(R_{v}\) refer to those on the validation set.  
\end{table} 
Table 8: Comparison of the experiment results between Figure 10, Figure 11, and validation set. We note that the annotation files of the BCCD dataset do not label all of the blood cells due to unknown reasons, significantly impacting the precision of all the models. Future WorkIn blood smear images, some blood cells are at the edge of the images. We are currently working on improving our architecture with the concept introduced in Mask R-CNN [50] to handle imperfect blood cells. As well, recent deep learning object detection models are under consideration. We also look for more datasets of blood smear images to include more blood cell samples for learning. Conceptualization, [PERSON]-[PERSON] and [PERSON]; methodology, S.-J.L. ; software, P.-Y.C. ; validation, [PERSON]-[PERSON] and [PERSON]-[PERSON]; writing--original draft preparation, P.-Y.C. ; writing--review and editing, [PERSON]-J.L. and [PERSON]-[PERSON]; visualization, P.-Y.C. ; supervision, [PERSON]-[PERSON] and [PERSON]. All authors have read and agreed to the published version of the manuscript.