TABLE III. Detection Accuracy for Our RBC Point Set. All Training Data of the Polygon Set (165 Images) has Been Used to Generate the Training Model to Test 800 Images From 160 Patients. The t-Tests Between Our Proposed Dual RBCNet and Other Methods Have P-Values < 0.001, Indicating That the Differences Between Groups are Statistically Significant.
Method  Evaluation Metrics |
F1-Measure 
|
Precision 
|
Recall 
|
|---|---|---|---|
| Traditional methods | |||
| Watershed [17] |  |
 |
 |
| Active contour[18], [19] |  |
 |
 |
| Instance segmentation deep learning methods | |||
| SegNet [48] |  |
 |
 |
| U-Net [42] |  |
 |
 |
| DeepLab v3+ [49] |  |
 |
 |
| Object detection deep learning methods | |||
| Faster R-CNN [39] on overlapping-tiles + NMS |  |
 |
 |
| Yolo [50] on overlapping-tiles + NMS |  |
 |
 |
| SSD [51] on overlapping-tiles + NMS |  |
 |
 |
| Mask R-CNN [52] on overlapping-tiles + NMS |  |
 |
 |
| Proposed dual deep learning networks | |||
| SegNet + Faster R-CNN |  |
 |
 |
| U-Net + YOLO |  |
 |
 |
| RBCNet (U-Net + Faster R-CNN) |  |
 |
 |