TumorModel.py

import torch.nn as nn

class TumorClassification(nn.Module):
    def __init__(self):
        super().__init__()
        self.model = nn.Sequential(
            nn.Conv2d(1, 16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(2),
            nn.Flatten(),
            nn.Linear(32 * 56 * 56, 128),
            nn.ReLU(),
            nn.Linear(128, 4)  # 4 classes: glioma, meningioma, notumor, pituitary
        )

    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)  # 4 glioma stages
        )

    def forward(self, x):
        return self.model(x)