[Retracted] Deep Learning Model for Automatic Classification and Prediction of Brain Tumor

Table 2

(a) Different architectures of CNN: DenseNet121 and DenseNet201

Layers	Output size	DenseNet121	DenseNet201
Convolution		, stride 2	, stride 2
Pooling		maxpool, stride 2	maxpool, stride 2
Dense block 1
Transitional layer 1
	Dense block 2
Transitional layer 2
	Dense block 3
Transitional layer 3
	Dense block 4
Classification layer
	1000	Fully connected, Softmax	Fully connected, Softmax

(b) Different architectures of CNN: VGG 16 and VGG 19

Layers	Output size	VGG 16	VGG 19
Convolution Block1	224×224
	112×112	Max pooling 2D	Max pooling 2D
Convolution Block2	112×112
	56×56	Max pooling 2D	Max pooling 2D
Convolution Block3	56×56
	28×28	Max pooling 2D	Max pooling 2D
Convolution Block4	28×28
	14×14	Max pooling 2D	Max pooling 2D
Convolution Block5	14×14
	7×7	Max pooling 2D	Max pooling 2D
Classification layer	4096

(c) Different architectures of CNN and parameters of all the models

Name of model	Size of input layer	Size of output layer	Number of layers	Trainable parameters (millions)
VGG16	(224, 224, 3)	(4,1)	16	138
VGG19	(224, 224, 3)	(4,1)	19	143
DenseNet121	(224, 224, 3)	(4,1)	121	8
DenseNet201	(224, 224, 3)	(4,1)	201	10.2