[Retracted] Design of Optimal Deep Learning-Based Pancreatic Tumor and Nontumor Classification Model Using Computed Tomography Scans

Table 3

Comparative classification results analysis of ODL-PTNTC technique under varying K folds.

No. of folds	ODL-PTNTC	DS-WELM	DS-KELM	DS-ELM
Sensitivity
K = 6	97.73	97.57	96.07	94.31
K = 7	97.61	96.03	96.54	97.41
K = 8	98.53	98.29	94.45	97.48
K = 9	96.70	96.44	96.05	93.75
K = 10	98.85	98.59	95.46	94.89
Average	97.88	97.38	95.71	95.57
Specificity
K = 6	99.42	99.27	96.25	98.03
K = 7	98.80	98.57	96.72	98.51
K = 8	99.45	96.99	98.72	99.37
K = 9	99.75	98.22	99.65	96.50
K = 10	99.47	97.88	99.32	97.06
Average	99.38	98.19	98.13	97.89
Accuracy
K = 6	99.77	98.46	99.34	93.79
K = 7	99.44	96.98	99.04	97.46
K = 8	96.23	95.46	96.10	94.22
K = 9	96.48	96.18	94.86	96.09
K = 10	98.47	97.15	93.89	98.29
Average	98.08	96.85	96.65	95.97
F-score
K = 6	98.24	97.99	98.1	96.57
K = 7	98.32	97.96	97.44	96.15
K = 8	98.05	97.84	97.64	96.92
K = 9	99.92	95.78	97.86	99.87
K = 10	98.63	98.21	95.31	96.00
Average	98.63	97.56	97.27	97.10