Rapid pathologic grading-based diagnosis of esophageal squamous cell carcinoma via Raman spectroscopy and a deep learning algorithm

Table 5 The performance metrics

Algorithm types	Accuracy	Specificity	Sensitivity	F1 score
PLS-Transformer	93.30 ± 2.53	96.65 ± 1.27	93.30 ± 2.54	93.17 ± 2.67
PLS-ResNet18	88.52 ± 5.44	94.28 ± 2.72	88.51 ± 5.50	88.57 ± 5.38
PLS-LSTM	80.18 ± 5.60	90.06 ± 3.11	80.09 ± 6.79	78.80 ± 6.43
PLS-GRU	88.58 ± 7.39	94.22 ± 4.02	88.47 ± 7.63	87.87 ± 7.46
PLS-EfficientNet	81.01 ± 6.10	90.64 ± 3.32	81.94 ± 5.91	80.72 ± 5.64
PLS-DenseNet	90.79 ± 3.94	95.29 ± 2.15	90.73 ± 4.09	90.62 ± 3.68
PLS-SVM	87.46 ± 7.51	94.04 ± 2.89	87.20 ± 7.16	87.30 ± 7.20
PLS-XGB	74.27 ± 5.86	85.00 ± 4.03	74.36 ± 5.09	74.4 ± 5.5

PLS: Partial least-squares; LSTM: Long Short-Term Memory; GRU: Gated Recurrent Units; SVM: Support vector machine; XGB: Extreme gradient boosting.