Research Article
Cell Phenotype Classification Based on Joint of Texture Information and Multilayer Feature Extraction in DenseNet
Table 5
Comparison results of state-of-the-art methods on the 2D-HeLa dataset in terms of accuracy, precision, and F1 score (%).
| Approach | Type of features | Accuracy (%) | Precision (%) | F1 score (%) |
| SAHLBP (BoW (VQ) + SPM + SVM) [17] | Handcrafted | 84.49 ± 2.2 | — | 83.75 | SIFT + SAHLBP (BoW (VQ) + SPM + SVM) [17] | Handcrafted | 86.20 ± 2.5 | — | 85.35 | MLBP8,1 + MLBP16,2 [18] | Handcrafted | 84.33 | — | — | MT-ULTP8,1 + MT-ULTP16,2 [18] | Handcrafted | 86.77 | — | — | Haralick-based SVM [21] | Handcrafted | 84.10 | — | — | Random subspace of LMC [32] | Handcrafted | 90.24 | — | — | AdaBoost ERC [32] | Handcrafted | 91.53 ± 0.02 | — | — | SIFT (BoW (LLC) + SPM + softmax) [33] | Handcrafted | 89.37 ± 1.5 | — | — | LTP16,2 [34] | Handcrafted | 87.00 | — | — | LBP-rotation invariant uniformLBP16,2 [34] | Handcrafted | 82.70 | — | — | Orthogonal locality preserving projection (OLPP) [34] | Handcrafted | 89.30 | — | — | Neighborhood preserving embedding (NPE) [34] | Handcrafted | 93.20 | — | — | Discriminative LBP [34] | Handcrafted | 84.5 | — | — | LBP-rotation invariant [34] | Handcrafted | 75.01 | — | — | Completed LBP [34] | Handcrafted | 88.8 | — | — | Random subspace ensemble of Levenberg–Marquardt neural network (RSNN) [16] | Classic neural nets | 85.00 | — | — | VGG-16 [1] | Deep | 72.10 ± 3.98 | — | — | VGG-16 + transfer learning [1] | Deep | 87.07 ± 2.86 | — | — | Inspection-v3 [1] | Deep | 83.18 ± 2.88 | — | — | Ensemble inception-v3 and Resnet152 and inception Resnet-v2 [20] | Deep | 93.51 ± 2.29 | — | — | Ensemble inception-v3 and Resnet152 [20] | Deep | 92.56 ± 1.92 | — | — | CapsNet [21] | Deep | 93.08 | — | — | Inspection-v3 + transfer learning [22] | Deep | 90.72 ± 1.85 | — | — | Inspection ResNet-v2 [22] | Deep | 92.00 ± 1.97 | — | — | DenseNet121 (baseline) | Deep | 91.87 ± 2.05 | 91.75 | 91.04 | DenseNet169 (baseline) | Deep | 92.63 ± 1.63 | 92.37 | 92.03 | DenseNet201 (baseline) | Deep | 91.72 ± 1.48 | 91.78 | 91.59 | DenseNet264 (baseline) | Deep | 90.98 ± 1.96 | 90.36 | 90.68 | ILQP + ML-DenseNet (proposed approach) | Handcrafted + deep | 93.36 ± 1.85 | 93.39 | 93.13 |
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