Table 3 Comparative analysis of various machine learning algorithms for fusion, illustrating the superior performance of the RF classifier in our study’s context. All values are presented as the average (Mean) with a measure of variability (Standard Deviation, SD). Significant values are in bold.
From: Precise grading of non-muscle invasive bladder cancer with multi-scale pyramidal CNN
Fusion ML algorithms | Sensitivity % | Specificity % | Accuracy % | F1-Score % |
|---|---|---|---|---|
RF | 94.47 ± 0.93 | 94.03 ± 0.95 | 94.25 ± 0.70 | 94.29 ± 0.70 |
Adaboost | 94.06 ± 1.04 | 93.50 ± 1.02 | 93.78 ± 0.80 | 93.82 ± 0.80 |
KNN | 94.62 ± 1.34 | 93.68 ± 1.01 | 94.15 ± 0.86 | 94.20 ± 0.86 |
MLP | 94.23 ± 0.97 | 93.19 ± 0.94 | 93.71 ± 0.64 | 93.77 ± 0.64 |
