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. 2024 Oct 24;31(11):6577–6597. doi: 10.3390/curroncol31110486

Table 1.

Summary of the considered state-of-the-art works.

Reference Methodologies Dataset(s) Outcomes
Sakri et al. [9] REPTree, NB, and KNN-IBK with PSO WPBC Accuracy: 81.3%, Precision: 88.3%, Recall: 93.4%, F-Score: 87.7%, AUC: 0.820
Alom et al. [10] Inception-v4, ResNet, and RCNN BreakHis and BCD Accuracy: 100%, Sensitivity: 100%, Specificity: 100%, AUC: 1.0
Hong et al. [11] LR and Gaussian mixture TCGA_TNBC, GEOD-40525, GSE40049 and GSE19783 AUC (GSE40049): 0.89, AUC (GSE19783): 0.90
Yan et al. [12] LR BCC at HMUCH AUC (HER2-Positive): 0.820, AUC (TNBC): 0.785
Mosayebi et al. [13] RF, LVQ, NB, C5.0 DT, MLP, KPCA-SVM, and SVM MHME-ICRC Accuracy: 81.9%, Sensitivity: 86.9%, Specificity:77.7%, F-Value: 81.6%, AUC: 0.774
Comes et al. [14] CNN and SVM I-SPY1 TRIAL and BREAST-MRI-NACT-Pilot Accuracy: 85.2%, Sensitivity: 84.6%, AUC: 0.83
Sanyal et al. [15] LSTM and XGBoost Manually and NLP-curated Sensitivity: 89.0%, Specificity: 84.0%, AUC: 0.94
Conde-Sousa et al. [16] DL HEROHE Precision: 79%, Recall: 100%, F-Value: 79%, AUC: 0.88
Rabinovici-Cohen et al. [17] CNN Real-World Retrospective Dataset Specificity: 57%, F-Value: 56%, Balanced Accuracy: 72%, PPV: 41%, NPV: 96%, Sensitivity: 93%, AUC: 0.75
Liu et al. [18] RF, LR, and XGBoost Clinical dataset Accuracy: 80%, Precision: 40%, Recall: 75%, F-Value: 50%, AUC: 0.75
TCGA Accuracy: 73%, Precision: 33%, Recall: 60%, F-Value: 42%, AUC: 0.72
Yang et al. [19] CNN and ResNet50 CAMS AUC: 0.76
TCGA AUC: 0.72
Lu et al. [20] GNN-based Slide Graph TCGA AUC: 0.75
HER2C and Nott-HER2 AUC: 0.80
Su et al. [21] CNN H&E and Ki67 Accuracy: 80%, F-Value: 79.2%, AUC: 0.811
Liu et al. [22] XGBoost AEHWU R-Square (CVDA): 0.9993, R-Square (Kmax): 0.9888
Botlagunta et al. [23] LR, KNN, DT, RF, SVM, GB, and XGBoost Medical data on MBC Accuracy: 83%, Precision: 83%, Recall: 100%, F-Value: 85%, AUC: 0.87
Dammu et al. [24] CNN I-SPY-1 TRIAL Accuracy: 81%, Sensitivity: 68%, Specificity: 97%, F-Value: 76%, AUC: 0.83