Table 6 Predictive performance of each class for VGG-16 architecture
From: Recognizing pathology of renal tumor from macroscopic cross-section image by deep learning
Metrics | pRCC | ccRCC | chRCC | OMT | AML | MCRN-LMP | RO | OBT |
|---|---|---|---|---|---|---|---|---|
AUC | 0.7940 | 0.8643 | 0.8379 | 0.9071 | 0.9872 | 0.9444 | 0.8958 | 0.8529 |
Sn | 0.2 | 0.8596 | 0.75 | 0.5 | 0.9231 | 1.0 | 0.6667 | 0.5 |
Sp | 0.9552 | 0.6 | 0.9375 | 0.974 | 1.0 | 0.9444 | 1.0 | 0.8824 |
Acc | 0.9028 | 0.8056 | 0.9167 | 0.9583 | 0.9474 | 0.9474 | 0.9474 | 0.8421 |