Table 3.

The results of classification metrics obtained for the test dataset using deep transfer learning systems based on different architecture of pretrained convolutional neural networks and also proposed ensemble method

	Micro precision	Micro recall	Micro F1-score	Accuracy
EfficientNetB0	84.74	84.61	84.16	85.92
EfficientNetB1	68.75	71.17	69.34	74.29
EfficientNetB2	87.19	86.66	86.76	88.5
EfficientNetB3	86.06	85.64	85.67	86.84
EfficientNetB4	88.11	88.51	88.27	89.42
EfficientNetB5	73.52	73.1	72.73	77.43
ResNet50	78.89	74.87	75.68	80.01
NASNetLarge	91.18	90.29	90.68	91.27
NASNetMobile	85.51	84.89	84.96	86.47
DenseNet121	81.19	82.97	81.72	83.7
Inception_resnet_v2	87.96	86.99	87.35	88.87
InceptionV3	87.71	86.61	87.03	88.13
Xception	86.85	85.79	86.04	87.76
ResNext50	83.58	82.17	82.41	85.36
SeResnet50	85.84	87.67	86.61	87.58
Proposed Ensemble model	91.94	91.94	91.94	91.94