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Brain_Tumor_model.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c167c5800f7d5db8d02760faad44a6362f8005664238c006b19c48d35e356568
+size 5123085

README.md

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 ---
-title: Tumor HENA
-emoji: 📊
+title: Brain Tumor Detection
+emoji: 🌍
 colorFrom: indigo
-colorTo: green
+colorTo: purple
 sdk: gradio
-sdk_version: 5.4.0
+sdk_version: 5.1.0
 app_file: app.py
 pinned: false
 license: mit
-short_description: brain tumor detention
+short_description: Brain Tumor Detection by CNN (PyTorch).
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import torch
+from PIL import Image
+import torchvision.transforms as transforms
+import gradio as gr
+import torch.nn.functional as F
+from torch import nn
+import numpy as np
+
+
+# Preprocess and load the model
+def preprocess_image(image):
+    transform = transforms.Compose([
+        transforms.Resize((256, 256)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
+    ])
+    return transform(image).unsqueeze(0)
+
+def findConv2dOutShape(hin,win,conv,pool=2):
+    # get conv arguments
+    kernel_size = conv.kernel_size
+    stride=conv.stride
+    padding=conv.padding
+    dilation=conv.dilation
+
+    hout=np.floor((hin+2*padding[0]-dilation[0]*(kernel_size[0]-1)-1)/stride[0]+1)
+    wout=np.floor((win+2*padding[1]-dilation[1]*(kernel_size[1]-1)-1)/stride[1]+1)
+
+    if pool:
+        hout/=pool
+        wout/=pool
+    return int(hout),int(wout)
+
+# Define Architecture For CNN_TUMOR Model
+class CNN_TUMOR(nn.Module):
+    
+    # Network Initialization
+    def __init__(self, params):
+        super(CNN_TUMOR, self).__init__()
+    
+        Cin,Hin,Win = params["shape_in"]
+        init_f = params["initial_filters"] 
+        num_fc1 = params["num_fc1"]  
+        num_classes = params["num_classes"] 
+        self.dropout_rate = params["dropout_rate"] 
+        
+        # Convolution Layers
+        self.conv1 = nn.Conv2d(Cin, init_f, kernel_size=3)
+        h,w=findConv2dOutShape(Hin,Win,self.conv1)
+        self.conv2 = nn.Conv2d(init_f, 2*init_f, kernel_size=3)
+        h,w=findConv2dOutShape(h,w,self.conv2)
+        self.conv3 = nn.Conv2d(2*init_f, 4*init_f, kernel_size=3)
+        h,w=findConv2dOutShape(h,w,self.conv3)
+        self.conv4 = nn.Conv2d(4*init_f, 8*init_f, kernel_size=3)
+        h,w=findConv2dOutShape(h,w,self.conv4)
+        
+        # compute the flatten size
+        self.num_flatten=h*w*8*init_f
+        self.fc1 = nn.Linear(self.num_flatten, num_fc1)
+        self.fc2 = nn.Linear(num_fc1, num_classes)
+
+    def forward(self,X):
+        # Convolution & Pool Layers
+        X = F.relu(self.conv1(X)); 
+        X = F.max_pool2d(X, 2, 2)
+        X = F.relu(self.conv2(X))
+        X = F.max_pool2d(X, 2, 2)
+        X = F.relu(self.conv3(X))
+        X = F.max_pool2d(X, 2, 2)
+        X = F.relu(self.conv4(X))
+        X = F.max_pool2d(X, 2, 2)
+        X = X.view(-1, self.num_flatten)
+        X = F.relu(self.fc1(X))
+        X = F.dropout(X, self.dropout_rate)
+        X = self.fc2(X)
+        return F.log_softmax(X, dim=1)
+
+# Define the parameters for the model
+params = {
+    "shape_in": (3,256,256), 
+    "initial_filters": 8,    
+    "num_fc1": 100,
+    "dropout_rate": 0.25,
+    "num_classes": 2
+}
+
+
+# Load the state dictionary
+model = CNN_TUMOR(params)  # Initialize the model with the correct architecture and parameters
+# Load the entire model
+model = torch.load('Brain_Tumor_model.pt', map_location=torch.device('cpu'))
+model.eval()
+
+
+# Function to make predictions
+def predict(image):
+    image_tensor = preprocess_image(image)
+    with torch.no_grad():
+        output = model(image_tensor)
+        _, predicted = torch.max(output, 1)
+
+        # Get the confidence score
+        confidence = F.softmax(output, dim=1)[0][predicted.item()].item() * 100  # Convert to percentage
+
+    # Generate a message based on the prediction
+    if predicted.item() == 1:
+        return f"No Tumor Detected (Confidence: {confidence:.2f}%)"
+    else:
+        return f"Tumor Detected (Confidence: {confidence:.2f}%)"
+
+# Create the Gradio interface
+interface = gr.Interface(
+    fn=predict, 
+    inputs=gr.Image(type="pil"), 
+    outputs="text", 
+    title="Brain Tumor Detection",
+    description="<div style='font-size: 24px; color: blue;'>"
+                "<strong>Upload a brain MRI image to detect if there is a tumor.</strong>"
+                "</div><br>",
+    article="<div style='font-size: 16px;'><strong><em>Sattorov Abdullah</strong></em></div>"
+)
+
+# Launch the interface
+interface.launch()

gitattributes

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requirements.txt

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+torch
+torchvision
+gradio
+numpy
+Pillow
+matplotlib
+