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BTD_model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:269506d46e3854e3561379a7d4eb977194e66430b781b36bc56fab481e224f17
+size 178115794

TumorModel.py

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+import torch.nn as nn
+
+class TumorClassification(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.con1d = nn.Conv2d(1, 32, 3, padding=1)
+        self.con2d = nn.Conv2d(32, 64, 3, padding=1)
+        self.con3d = nn.Conv2d(64, 128, 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.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.pool(self.relu(self.con1d(x)))
+        x = self.pool(self.relu(self.con2d(x)))
+        x = self.pool(self.relu(self.con3d(x)))
+        x = x.view(x.size(0), -1)
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        return self.output(x)
+
+class GliomaStageModel(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.fc1 = nn.Linear(9, 100)
+        self.fc2 = nn.Linear(100, 50)
+        self.fc3 = nn.Linear(50, 30)
+        self.out = nn.Linear(30, 4)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x = self.relu(self.fc1(x))
+        x = self.relu(self.fc2(x))
+        x = self.relu(self.fc3(x))
+        return self.out(x)

app.py

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+import streamlit as st
+import torch
+import torch.nn as nn
+from torchvision import transforms
+from PIL import Image
+from TumorModel import TumorClassification, GliomaStageModel
+
+# Title
+st.set_page_config(page_title="Brain Tumor Predictor", layout="centered")
+st.title("🧠 Brain Tumor Classifier & Glioma Stage Predictor")
+
+# Load models
+@st.cache_resource
+def load_models():
+    tumor_model = TumorClassification()
+    tumor_model.load_state_dict(torch.load("BTD_model.pth", map_location=torch.device("cpu")))
+    tumor_model.eval()
+
+    glioma_model = GliomaStageModel()
+    glioma_model.load_state_dict(torch.load("glioma_stages.pth", map_location=torch.device("cpu")))
+    glioma_model.eval()
+
+    return tumor_model, glioma_model
+
+tumor_model, glioma_model = load_models()
+
+# Labels
+tumor_labels = ['glioma', 'meningioma', 'notumor', 'pituitary']
+stage_labels = ['Stage 1', 'Stage 2', 'Stage 3', 'Stage 4']
+
+# Image Preprocessing
+transform = transforms.Compose([
+    transforms.Grayscale(),
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.5], [0.5])
+])
+
+# Tabs
+tab1, tab2 = st.tabs(["🧠 Tumor Type Detection", "🧬 Glioma Stage Prediction"])
+
+# Tab 1: Tumor Classification
+with tab1:
+    st.header("Upload Brain MRI")
+    image_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    if image_file:
+        image = Image.open(image_file).convert("L")
+        st.image(image, caption="Uploaded Image", use_column_width=True)
+
+        image_tensor = transform(image).unsqueeze(0)
+        with torch.no_grad():
+            prediction = tumor_model(image_tensor)
+            pred_label = tumor_labels[torch.argmax(prediction)]
+        st.success(f"🧠 Tumor Prediction: **{pred_label.upper()}**")
+
+# Tab 2: Glioma Stage Prediction
+with tab2:
+    st.header("Patient & Genetic Info")
+    gender = st.radio("Gender", ["Male", "Female"])
+    age = st.slider("Age", 1, 100, 25)
+
+    st.subheader("Gene Mutations (0 = No, 1 = Yes)")
+    idh1 = st.radio("IDH1", [0, 1], horizontal=True)
+    tp53 = st.radio("TP53", [0, 1], horizontal=True)
+    atrx = st.radio("ATRX", [0, 1], horizontal=True)
+    pten = st.radio("PTEN", [0, 1], horizontal=True)
+    egfr = st.radio("EGFR", [0, 1], horizontal=True)
+    cic = st.radio("CIC", [0, 1], horizontal=True)
+    pik3ca = st.radio("PIK3CA", [0, 1], horizontal=True)
+
+    if st.button("🔍 Predict Glioma Stage"):
+        gender_val = 0 if gender == "Male" else 1
+        features = torch.tensor([[gender_val, age, idh1, tp53, atrx, pten, egfr, cic, pik3ca]], dtype=torch.float32)
+        with torch.no_grad():
+            prediction = glioma_model(features)
+            stage = stage_labels[torch.argmax(prediction)]
+        st.success(f"🧬 Predicted Glioma Stage: **{stage}**")

glioma_stages.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c64f506838505e6cfa74775ceed9260b03a978bf64243d123396393932cb1a2b
+size 33544