Table 5.

Computational time.

Deep learning models	Convolution Layer output	Feature size	Testing time milliseconds(ms)
Vgg16	4, 4, 512	8192	101549.47
Vgg19	4, 4, 512	8192	11425.91
EfficiebtnetB0	5, 5, 1280	32000	406196.04
ResNet50	5, 5, 2048	51200	609294.78
Hybrid deep Learning model (max pooling layer 2 $\times \mathbf{2}$)-NN	Vgg16 (2, 2, 512) Vgg19 (2, 2, 512)	4096	30217.18
Hybrid deep learning model(average pooling layer)- Naive Bayes			30069.80
Hybrid deep learning model(average pooling layer)-Random Forest			28290.65
Hybrid deep learning model(average pooling layer)-KNN			364.87
Hybrid deep learning model(average pooling layer)-SVM (rbf)			424.26
Hybrid deep learning model(average pooling layer)-SVM (sigmoid)			38316.83
Hybrid deep learning model(average pooling layer)-SVM (linear)			171267.67
Hybrid deep learning model (average pooling layer 2 $\times \mathbf{2}$)-NN			32185.88