Table 9 State-of-the-art Comparison.
From: A quantum-optimized approach for breast cancer detection using SqueezeNet-SVM
Ref # | Methodology | ACC% | Sen% | Spc% |
|---|---|---|---|---|
D-T | 95.8 | – | – | |
GSL | 75.05 | – | – | |
FCM-ELM | 96.46 | – | – | |
GL-SGL1/2 | 92.42 | – | – | |
L-FA | 97.28 | – | – | |
ELM-SPI | 96.43 | – | – | |
PML-Net | 94.0 | – | – | |
Kmean-SVM | 97.28 | – | – | |
GA-OGB | 94.28 | 93.20 | 93.11 | |
LF-ANN | 97 | 94.00 | – | |
SVM-AR | 98.00 | 98.03 | 97.93 | |
N-B | 98 | – | – | |
j-48 | 97.54 | – | – | |
SMO | 98.73 | – | – | |
CNN | 92.54 | – | – | |
FA-EMODE | 96.86 | – | – | |
DBN-SFO | 91.5 | 94.1 | 72.4 | |
Disep-Net | 95.60 | – | – | |
DT-AFIS | 95.91 | – | – | |
LR + SVM | 98.77 | – | – | |
BC-NET | 98.01 | 97.75 | 98.16 | |
GWO-SVM | 97.33 | 95.96 | – | |
Proposed | Q-BGWO-SQSVM | 99.00 | 98.00 | 1.00 |
