Table 3 Features selected by PySckit-Library according to their performance with the shown structure.
Model | Features | Mel Frequency Cepstral Coefficient | Indices of Selected Features |
|---|---|---|---|
CNN design | [13,14,15] | 5 | [2,9,12,27,28,31] |
VGG + ConvNets | [13,14,15] | 7 | [1,3,5,7,22,27,28] |
Support Vector Machine (SVM) classifier | [13,14,15] | 11 | [3,10,13,28,29,31] |
NNU-Net | [13,14,15] | 6 | [2,6,9,5,8,9,16] |
SVM + CNN | [13,14,15] | 6 | [3,10,13,28,29,32] |
RGA-Unet | [13,14,15] | 7 | [1,3,6,9,16,19] |
InceptionResNetV2 | [13,14,15] | 5 | [3,10,13,27,29,31] |
VGG-16 | [13,14,15] | 5 | [1,3,5,7,22,27,28] |
U-Net++ | [13,14,15] | 7 | [1,3,5,7,22,27,28] |
3D U-Net | [13,14,15] | 6 | [1,3,6,9,16,19] |
Segmentation network U-Net | [13,14,15] | 5 | [2,9,12,27,28,31] |
ResNet50-UNet | [13,14,15] | 7 | [2,6,9,5,8,9,16] |
CNN-Based Inception-V3 | [13,14,15] | 11 | [3,10,13,28,29,32] |
Hyperparameter-Tuned CNN | [13,14,15] | 5 | [3,10,13,28,29,32] |
RCS-YOLO | [13,14,15] | 6 | [1,3,6,9,17,19,30] |
AlexNet-SVM and AlexNet-KNN | [13,14,15] | 7 | [1,3,6,9,16,19] |
Proposed Model | [13,14,15] | 6 | [2,9,12,27,28,31] |
