Dataset-
It is a medical images directory structure branched into 3 subfolders (COVID, NORMAL, PNEUMONIA) which contains the Chest X-ray (CXR) Images.
COVID: 1626 images
NORMAL: 1802 images
PNEUMONIA: 1800 images
Original Dataset Link- https://www.kaggle.com/datasets/sachinkumar413/covid-pneumonia-normal-chest-xray-images
In the Dataset folder contains customized dataset which is splitting the whole dataset into three parts-
1- Training (70%)
2- Validation (15%)
3- Testing (15%)
Model Architecture:
CNN(
(conv_layers): Sequential(
(0): Conv2d(3, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU()
(2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(3): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(4): ReLU()
(5): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(6): Conv2d(32, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(7): ReLU()
(8): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(9): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(10): ReLU()
(11): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(fc_layers): Sequential(
(0): Linear(in_features=25088, out_features=512, bias=True)
(1): ReLU()
(2): Dropout(p=0.2, inplace=False)
(3): Linear(in_features=512, out_features=256, bias=True)
(4): ReLU()
(5): Dropout(p=0.2, inplace=False)
(6): Linear(in_features=256, out_features=128, bias=True)
(7): ReLU()
(8): Dropout(p=0.2, inplace=False)
(9): Linear(in_features=128, out_features=3, bias=True)
)
)
There are 4 Conv2d layers and 4 Linear Layer and use MaxPool in the pooling layer
| Layer (type) | Output Shape | Param # |
|---|---|---|
| Conv2d-1 | [-1, 16, 224, 224] | 448 |
| ReLU-2 | [-1, 16, 224, 224] | 0 |
| MaxPool2d-3 | [-1, 16, 112, 112] | 0 |
| Conv2d-4 | [-1, 32, 112, 112] | 4,640 |
| ReLU-5 | [-1, 32, 112, 112] | 0 |
| MaxPool2d-6 | [-1, 32, 56, 56] | 0 |
| Conv2d-7 | [-1, 64, 56, 56] | 18,496 |
| ReLU-8 | [-1, 64, 56, 56] | 0 |
| MaxPool2d-9 | [-1, 64, 28, 28] | 0 |
| Conv2d-10 | [-1, 128, 28, 28] | 73,856 |
| ReLU-11 | [-1, 128, 28, 28] | 0 |
| MaxPool2d-12 | [-1, 128, 14, 14] | 0 |
| Linear-13 | [-1, 512] | 12,845,568 |
| ReLU-14 | [-1, 512] | 0 |
| Dropout-15 | [-1, 512] | 0 |
| Linear-16 | [-1, 256] | 131,328 |
| ReLU-17 | [-1, 256] | 0 |
| Dropout-18 | [-1, 256] | 0 |
| Linear-19 | [-1, 128] | 32,896 |
| ReLU-20 | [-1, 128] | 0 |
| Dropout-21 | [-1, 128] | 0 |
| Linear-22 | [-1, 3] | 387 |
| Total params | 13,107,619 | |
| Trainable params | 13,107,619 | |
| Non-trainable params | 0 | |
| Input size (MB) | 0.57 | |
| Forward/backward pass size (MB) | 25.86 | |
| Params size (MB) | 50.00 | |
| Estimated Total Size (MB) | 76.44 |