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import torch.nn as nn
import torch.nn.functional as F
# Tumor Classification CNN
class TumorClassification(nn.Module):
def __init__(self):
super(TumorClassification, self).__init__()
self.con1d = nn.Conv2d(1, 32, kernel_size=3, padding=1)
self.con2d = nn.Conv2d(32, 64, kernel_size=3, padding=1)
self.con3d = nn.Conv2d(64, 128, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(128 * 29 * 29, 512)
self.fc2 = nn.Linear(512, 256)
self.output = nn.Linear(256, 4)
def forward(self, x):
x = self.pool(F.relu(self.con1d(x)))
x = self.pool(F.relu(self.con2d(x)))
x = self.pool(F.relu(self.con3d(x)))
x = x.view(x.size(0), -1)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
return self.output(x)
# Glioma Stage Predictor
class GliomaStageModel(nn.Module):
def __init__(self):
super(GliomaStageModel, self).__init__()
self.fc1 = nn.Linear(9, 100)
self.fc2 = nn.Linear(100, 50)
self.fc3 = nn.Linear(50, 30)
self.out = nn.Linear(30, 4)
def forward(self, x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = F.relu(self.fc3(x))
return self.out(x)
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