Update TumorModel.py

TumorModel.py

@@ -1,24 +1,29 @@
 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, 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 * 28 * 28, 512)
-        self.fc2 = nn.Linear(512, 256)
-        self.output = nn.Linear(256, 4)
+        self.model = nn.Sequential(
+            nn.Conv2d(1, 32, 3, 1, 1),  # con1d
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            nn.Conv2d(32, 64, 3, 1, 1), # con2d
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            nn.Conv2d(64, 128, 3, 1, 1),# con3d
+            nn.ReLU(),
+            nn.MaxPool2d(2),
+            nn.Flatten(),
+            nn.Linear(128 * 26 * 26, 512),  # fc1
+            nn.ReLU(),
+            nn.Linear(512, 256),            # fc2
+            nn.ReLU(),
+            nn.Linear(256, 4)               # output
+        )
 
     def forward(self, x):
-        x = nn.functional.relu(self.pool(self.con1d(x)))
-        x = nn.functional.relu(self.pool(self.con2d(x)))
-        x = nn.functional.relu(self.pool(self.con3d(x)))
-        x = x.view(x.size(0), -1)
-        x = nn.functional.relu(self.fc1(x))
-        x = nn.functional.relu(self.fc2(x))
-        return self.output(x)
+        return self.model(x)
 
 class GliomaStageModel(nn.Module):
     def __init__(self):
@@ -26,7 +31,7 @@ class GliomaStageModel(nn.Module):
         self.fc1 = nn.Linear(9, 100)
         self.fc2 = nn.Linear(100, 50)
         self.fc3 = nn.Linear(50, 30)
-        self.out = nn.Linear(30, 2)  # ✅ Make sure this matches your trained model
+        self.out = nn.Linear(30, 2)
 
     def forward(self, x):
         x = nn.functional.relu(self.fc1(x))