Research Article
[Retracted] Deep Learning Model for Automatic Classification and Prediction of Brain Tumor
(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 |
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(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 | | |
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(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 |
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