Update TumorModel.py

TumorModel.py

@@ -1,39 +1,10 @@
-import torch.nn as nn
-import torch
-
-class TumorClassification(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.con1d = nn.Conv2d(1, 32, kernel_size=3, stride=1, padding=1)
-        self.con2d = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
-        self.con3d = nn.Conv2d(64, 128, kernel_size=3, stride=1, padding=1)
-        
-        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
-
-        self.fc1 = nn.Linear(128 * 26 * 26, 512)  # Must match training input size (208x208 image)
-        self.fc2 = nn.Linear(512, 256)
-        self.output = nn.Linear(256, 4)
-
-        self.relu = nn.ReLU()
-
-    def forward(self, x):
-        x = self.pool(self.relu(self.con1d(x)))
-        x = self.pool(self.relu(self.con2d(x)))
-        x = self.pool(self.relu(self.con3d(x)))
-        x = x.view(x.size(0), -1)
-        x = self.relu(self.fc1(x))
-        x = self.relu(self.fc2(x))
-        x = self.output(x)
-        return x
-
-
 class GliomaStageModel(nn.Module):
     def __init__(self):
         super().__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)  # 4 stages
+        self.out = nn.Linear(30, 2)  # ✅ MATCHING saved model weights
 
     def forward(self, x):
         x = nn.functional.relu(self.fc1(x))