Reference | Technique Used | Segmentation | Classification | Performance Parameters | |||
---|---|---|---|---|---|---|---|
Accuracy (%) | Precision (%) | Sensitivity (%) | F1-Score | ||||
Liang et al. [21] | CNN-RNN | × | ✓ | 95.42 | 91.75 | 96.91 | 89.0 |
Pang et al. [22] | Fusion of Shallow and Deep Feature Maps | × | ✓ | 94.33 | 91.25 | 96.00 | 89.0 |
Yu et al. [23] | Deep Neural Network | × | ✓ | 90.51 | 81.5 | 92.39 | 81.0 |
Banik et al. [24] | Feature Map Fusion of Two Convolution Layers | × | ✓ | 96.01 | 97 | 99.03 | 92.0 |
Proposed Model | Deep Feature Map Extraction of Segmented Leukocyte | ✓ | ✓ | 97.98 | 97.97 | 97 | 97.0 |