Table 5.

Transfer learning model incorporating ResNet-50, ResNet-101, and EfficientNet-B3 with the specified configurations

Layer (type)	Output Shape	Param #	Connected to
input_image (InputLayer)	(224, 224, 3)	0	–
resnet50_base (Functional)	(7, 7, 2048)	23,587,712	input_image[0][0]
resnet101_base (Functional)	(7, 7, 2048)	42,658,176	input_image[0][0]
efficientnetb3_base (Functional)	(7, 7, 1536)	10,783,535	input_image[0][0]
global_average_pooling2d	Global (2048)	0	resnet50_base[0][0]
global_average_pooling 2d_1	Global (2048)	0	resnet101_base[0][0]
global_average_pooling2d_2	Global (1536)	0	efficientnetb3_base[0][0]
dense_layer_1 (Dense)	(128)	262,272	global_average_pooling2d[0][0]
dense_layer_3 (Dense)	(128)	262,272	global_average_pooling2d_1[0][0]
dense_layer_5 (Dense)	(128)	196,736	global_average_pooling2d_2[0][0]
dropout_1 (Dropout)	(128)	0	dense_layer_1[0][0]
dropout_3 (Dropout)	(128)	0	dense_layer_3[0][0]
dropout_5 (Dropout)	(128)	0	dense_layer_5[0][0]
dense_layer_2 (Dense)	(64)	8256	dropout_1[0][0]
dense_layer_4 (Dense)	(64)	8256	dropout_3[0][0]
dense_layer_6 (Dense)	(64)	8256	dropout_5[0][0]
dropout_2 (Dropout)	(64)	0	dense_layer_2[0][0]
dropout_4 (Dropout)	(64)	0	dense_layer_4[0][0]
dropout_6 (Dropout)	(64)	0	dense_layer_6[0][0]
output_layer (Dense)	(4)	260	dropout_2[0][0] dropout_4[0][0] dropout_6[0][0]
output_activation (Activation)	(4)	0	output_layer[0][0] output_layer[1][0] output_layer[2][0]