Research Article
Computational Intelligence Method for Detection of White Blood Cells Using Hybrid of Convolutional Deep Learning and SIFT
Table 4
Comparing the accuracy of the proposed method in detecting the white blood cells with four baseline methods.
| Reference | Segmentation method | Classification method | Sample size | Accuracy |
| The proposed method | Gram-Schmidt orthogonalization | WTPSSR | 260 | 97.14% | The proposed model by Rezatofighi et al. [17] | Gram-Schmidt orthogonalization and snake | SVM | 400 | 86.10% | The proposed model by Zhang et al. [26] | Histogram threshold | Distance classifier | 199 | 92.46% | The proposed model by Balki et al. [6] | Entropy threshold and iterative threshold | Distance classifier | 71 | 90.14% | The proposed model by Horne et al. [2] | Gram-Schmidt orthogonalization and snake | LVQ | 400 | 94.10% |
|
|