Update TumorModel.py

TumorModel.py

@@ -1,26 +1,26 @@
-# 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.con1d = nn.Conv2d(1, 32, 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.con2d = nn.Conv2d(32, 64, 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.con3d = nn.Conv2d(64, 128, 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.fc1 = nn.Linear(128 * 28 * 28, 512)  # 86528 = 128 * 28 * 28
         self.relu_fc = nn.ReLU()
-        self.fc2 = nn.Linear(128, 4)
+        self.fc2 = nn.Linear(512, 256)
+        self.relu_fc2 = nn.ReLU()
+        self.output = nn.Linear(256, 4)
 
     def forward(self, x):
         x = self.pool1(self.relu1(self.con1d(x)))
@@ -28,13 +28,14 @@ class TumorClassification(nn.Module):
         x = self.pool3(self.relu3(self.con3d(x)))
         x = self.flatten(x)
         x = self.relu_fc(self.fc1(x))
-        x = self.fc2(x)
+        x = self.relu_fc2(self.fc2(x))
+        x = self.output(x)
         return x
 
 
 class GliomaStageModel(nn.Module):
     def __init__(self):
-        super().__init__()
+        super(GliomaStageModel, self).__init__()
         self.model = nn.Sequential(
             nn.Linear(9, 128),
             nn.ReLU(),