Update TumorModel.py

TumorModel.py

@@ -2,21 +2,34 @@ 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)
-        )
+        super(TumorClassification, self).__init__()
+        self.con1d = nn.Conv2d(1, 32, kernel_size=3, padding=1)
+        self.relu1 = nn.ReLU()
+        self.pool1 = nn.MaxPool2d(2)
+
+        self.con2d = nn.Conv2d(32, 64, kernel_size=3, padding=1)
+        self.relu2 = nn.ReLU()
+        self.pool2 = nn.MaxPool2d(2)
+
+        self.con3d = nn.Conv2d(64, 128, kernel_size=3, padding=1)
+        self.relu3 = nn.ReLU()
+        self.pool3 = nn.MaxPool2d(2)
+
+        self.flatten = nn.Flatten()
+        self.fc1 = nn.Linear(128 * 28 * 28, 512)
+        self.relu4 = nn.ReLU()
+        self.fc2 = nn.Linear(512, 256)
+        self.relu5 = nn.ReLU()
+        self.output = nn.Linear(256, 4)
+
     def forward(self, x):
-        return self.model(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.relu4(self.fc1(x))
+        x = self.relu5(self.fc2(x))
+        return self.output(x)
 
 
 class GliomaStageModel(nn.Module):
@@ -28,7 +41,7 @@ class GliomaStageModel(nn.Module):
         self.relu2 = nn.ReLU()
         self.fc3 = nn.Linear(50, 30)
         self.relu3 = nn.ReLU()
-        self.out = nn.Linear(30, 2)  # only 2 classes in your .pth
+        self.out = nn.Linear(30, 2)
 
     def forward(self, x):
         x = self.relu1(self.fc1(x))