Update TumorModel.py

TumorModel.py

@@ -1,34 +1,47 @@
-import torch.nn as nn
-
-class TumorClassification(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.model = nn.Sequential(
-            nn.Conv2d(1, 16, 3, 1, 1),
-            nn.ReLU(),
-            nn.MaxPool2d(2),
-            nn.Conv2d(16, 32, 3, 1, 1),
-            nn.ReLU(),
-            nn.MaxPool2d(2),
-            nn.Flatten(),
-            nn.Linear(32 * 56 * 56, 128),
-            nn.ReLU(),
-            nn.Linear(128, 4)
-        )
-
-    def forward(self, x):
-        return self.model(x)
-
-class GliomaStageModel(nn.Module):
-    def __init__(self):
-        super().__init__()
-        self.model = nn.Sequential(
-            nn.Linear(9, 128),
-            nn.ReLU(),
-            nn.Linear(128, 64),
-            nn.ReLU(),
-            nn.Linear(64, 4)
-        )
-
-    def forward(self, x):
-        return self.model(x)
+# TumorModel.py
+
+import torch.nn as nn
+
+class TumorClassification(nn.Module):
+    def __init__(self):
+        super(TumorClassification, self).__init__()
+        self.con1d = nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1)
+        self.relu1 = nn.ReLU()
+        self.pool1 = nn.MaxPool2d(2)
+
+        self.con2d = nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1)
+        self.relu2 = nn.ReLU()
+        self.pool2 = nn.MaxPool2d(2)
+
+        self.con3d = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.relu3 = nn.ReLU()
+        self.pool3 = nn.MaxPool2d(2)
+
+        self.flatten = nn.Flatten()
+        self.fc1 = nn.Linear(64 * 28 * 28, 128)  # Adjusted if input is (1, 224, 224)
+        self.relu_fc = nn.ReLU()
+        self.fc2 = nn.Linear(128, 4)
+
+    def forward(self, x):
+        x = self.pool1(self.relu1(self.con1d(x)))
+        x = self.pool2(self.relu2(self.con2d(x)))
+        x = self.pool3(self.relu3(self.con3d(x)))
+        x = self.flatten(x)
+        x = self.relu_fc(self.fc1(x))
+        x = self.fc2(x)
+        return x
+
+
+class GliomaStageModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.model = nn.Sequential(
+            nn.Linear(9, 128),
+            nn.ReLU(),
+            nn.Linear(128, 64),
+            nn.ReLU(),
+            nn.Linear(64, 4)
+        )
+
+    def forward(self, x):
+        return self.model(x)