CNN Based Multiclass Brain Tumor Detection Using Medical Imaging

Table 1

The summary of model description.

Layer type	Filter	Kernel size	Output shape	Param#
Input layer	—	—	224 × 224 × 3	0-
Convolution	64	3 × 3	224 × 224 × 64	1792
Activation	—	—	224 × 224 × 64	0
BN	—	—	224 × 224 × 64	256
Convolution	64	3 × 3	222 × 222 × 64	36928
Activation	—	—	222 × 222 × 64	0
Max pooling	1	2 × 2	111 × 111 × 64	0
BN	—	—	111 × 111 × 64	256
Dropout	—	—	111 × 111 × 64	0
Convolution	64	3 × 3	109 × 109 × 64	36928
Activation	—	—	109 × 109 × 64	0
Max pooling	1	2 × 2	54 × 54 × 64	0
BN	—	—	54 × 54 × 64	256
Dropout	—	—	54 × 54 × 64	0
Convolution	64	3 × 3	54 × 54 × 64	36928
Activation	—	—	54 × 54 × 64	0
BN	—	—	54 × 54 × 64	256
Flatten	—	—	186624	0
Dropout	—	—	186624	0
FC	—	—	512	95552000
Activation	—	—	512	0
BN	—	—	512	2048
Output layer	—	—	4	2052
Total params: 95,706,884
Trainable params: 95,705,220
Nontrainable params: 1,664