Table 2.
Configurations of convolutional neural networks used in this work
| Architectures | Highlights | Configurations | Number of features extracted |
|---|---|---|---|
| VGG [29] | Factorized Convolution, a regularization strategy to avoid overfitting | VGG16 | 512 |
| VGG19 | 512 | ||
| Inception [30] | Inception Module, a building block for reducing the amount of extracted parameters | InceptionV3 | 2048 |
| InceptionResNetV2 | 1536 | ||
| ResNet [13] | Residual Block, the building block focused on vanishing-gradient optimizing | ResNet50 | 2048 |
| NASNet [35] | NASNet search space, a new architecture model build from the dataset of interest | NASNetLarge | 4032 |
| NASNetMobile | 1056 | ||
| Xception [3] | Depthwise Separable Convolution layers, the spatial and cross-channel correlation is separated | Xception | 2048 |
| MobileNet [14] | Two news hyper-parameters in the Xception model, which are Width Multiplier, Resolution Multiplier | MobileNet | 1024 |
| MobileNetV2 | 1280 | ||
| DenseNet [15] | Dense Block, a block that covers interconnects all layers | DenseNet121 | 1024 |
| DenseNet169 | 1664 | ||
| DenseNet201 | 1920 |