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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<title>NeuroScan | Brain Tumor Detection</title>
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background-color: #EFF6FF;
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.segmentation-map {
background: linear-gradient(135deg, #6B7280 0%, #9CA3AF 100%);
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.topology-features {
background: linear-gradient(135deg, #1E40AF 0%, #3B82F6 100%);
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height: 4px;
background: rgba(59, 130, 246, 0.7);
box-shadow: 0 0 10px rgba(59, 130, 246, 0.9);
animation: scan 3s linear infinite;
}
@keyframes scan {
0% { top: 0; }
100% { top: 100%; }
}
</style>
</head>
<body class="bg-gray-50 min-h-screen">
<!-- Header -->
<header class="bg-indigo-900 text-white shadow-lg">
<div class="container mx-auto px-4 py-6">
<div class="flex justify-between items-center">
<div class="flex items-center space-x-2">
<i class="fas fa-brain text-3xl text-blue-300"></i>
<h1 class="text-2xl font-bold">Neuro<span class="text-blue-300">Scan</span></h1>
</div>
<nav>
<ul class="flex space-x-6">
<li><a href="#" class="hover:text-blue-300 transition">Home</a></li>
<li><a href="#" class="hover:text-blue-300 transition">About</a></li>
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<section class="bg-gradient-to-r from-indigo-900 to-blue-800 text-white py-16">
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<h2 class="text-4xl font-bold mb-4">Advanced Brain Tumor Detection</h2>
<p class="text-xl mb-8 max-w-3xl mx-auto">Using topological image segmentation to identify tumors with 94% accuracy</p>
<div class="flex justify-center space-x-4">
<button class="bg-blue-500 hover:bg-blue-600 px-6 py-3 rounded-lg font-medium transition transform hover:scale-105">
Upload Scan
</button>
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Learn More
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<main class="container mx-auto px-4 py-12">
<div class="grid grid-cols-1 lg:grid-cols-3 gap-8">
<!-- Left Column - Upload Area -->
<div class="lg:col-span-1 bg-white rounded-xl shadow-lg p-6">
<h3 class="text-2xl font-bold text-gray-800 mb-6">Upload MRI Scan</h3>
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<div class="flex flex-col items-center justify-center h-full">
<i class="fas fa-cloud-upload-alt text-4xl text-gray-400 mb-4"></i>
<p class="text-gray-600 mb-2">Drag & drop your MRI scan here</p>
<p class="text-sm text-gray-500">or click to browse files</p>
<input type="file" id="fileInput" class="hidden" accept="image/*">
</div>
</div>
<div class="bg-blue-50 rounded-lg p-4 mb-6">
<h4 class="font-medium text-blue-800 mb-2">Scan Requirements</h4>
<ul class="text-sm text-blue-700 space-y-1">
<li><i class="fas fa-check-circle text-blue-500 mr-2"></i> DICOM or PNG format</li>
<li><i class="fas fa-check-circle text-blue-500 mr-2"></i> Minimum 256×256 resolution</li>
<li><i class="fas fa-check-circle text-blue-500 mr-2"></i> Axial view preferred</li>
</ul>
</div>
<button id="analyzeBtn" class="w-full bg-blue-600 hover:bg-blue-700 text-white py-3 rounded-lg font-medium transition flex items-center justify-center disabled:opacity-50 disabled:cursor-not-allowed" disabled>
<i class="fas fa-search mr-2"></i> Analyze Scan
</button>
</div>
<!-- Middle Column - Results -->
<div class="lg:col-span-2 space-y-8">
<!-- Original Image -->
<div class="bg-white rounded-xl shadow-lg overflow-hidden">
<div class="bg-gray-800 text-white px-4 py-3 flex justify-between items-center">
<h3 class="font-medium">Original MRI Scan</h3>
<div class="text-sm text-gray-300">No image uploaded</div>
</div>
<div id="originalImageContainer" class="relative h-64 bg-gray-100 flex items-center justify-center">
<div id="scanPlaceholder" class="text-center p-4">
<i class="fas fa-image text-5xl text-gray-300 mb-4"></i>
<p class="text-gray-500">Your scan will appear here</p>
</div>
<img id="originalImage" class="hidden max-h-full max-w-full" src="" alt="Uploaded MRI Scan">
</div>
</div>
<!-- Segmentation Results -->
<div id="resultsSection" class="hidden">
<div class="grid grid-cols-1 md:grid-cols-2 gap-6">
<!-- Segmentation Map -->
<div class="bg-white rounded-xl shadow-lg overflow-hidden">
<div class="bg-gray-700 text-white px-4 py-3">
<h3 class="font-medium">Segmentation Map</h3>
</div>
<div class="segmentation-map p-4 h-64 relative overflow-hidden">
<div id="scanLine" class="scan-line hidden"></div>
<canvas id="segmentationCanvas" class="max-h-full max-w-full mx-auto hidden"></canvas>
<div id="segmentationPlaceholder" class="text-center text-white p-4">
<i class="fas fa-spinner fa-spin text-3xl mb-3"></i>
<p>Analyzing topological features...</p>
</div>
</div>
</div>
<!-- Topology Features -->
<div class="bg-white rounded-xl shadow-lg overflow-hidden">
<div class="topology-features text-white px-4 py-3">
<h3 class="font-medium">Topological Features</h3>
</div>
<div class="bg-blue-50 p-4 h-64 overflow-y-auto">
<div id="featuresPlaceholder" class="text-center text-blue-800 p-4">
<i class="fas fa-cogs text-3xl mb-3"></i>
<p>Computing persistence diagrams...</p>
</div>
<div id="featuresResults" class="hidden space-y-4">
<div class="bg-white rounded-lg p-3 shadow">
<div class="flex justify-between items-center mb-1">
<span class="font-medium text-blue-800">Betti Numbers</span>
<span class="text-xs bg-blue-100 text-blue-800 px-2 py-1 rounded">β0=3, β1=2</span>
</div>
<div class="h-2 bg-gray-200 rounded-full overflow-hidden">
<div class="h-full bg-blue-500 rounded-full" style="width: 75%"></div>
</div>
</div>
<div class="bg-white rounded-lg p-3 shadow">
<div class="flex justify-between items-center mb-1">
<span class="font-medium text-blue-800">Persistence Diagram</span>
<span class="text-xs bg-blue-100 text-blue-800 px-2 py-1 rounded">3 components</span>
</div>
<div class="h-2 bg-gray-200 rounded-full overflow-hidden">
<div class="h-full bg-blue-500 rounded-full" style="width: 60%"></div>
</div>
</div>
<div class="bg-white rounded-lg p-3 shadow">
<div class="flex justify-between items-center mb-1">
<span class="font-medium text-blue-800">Euler Characteristic</span>
<span class="text-xs bg-blue-100 text-blue-800 px-2 py-1 rounded">χ=1</span>
</div>
<div class="h-2 bg-gray-200 rounded-full overflow-hidden">
<div class="h-full bg-blue-500 rounded-full" style="width: 85%"></div>
</div>
</div>
</div>
</div>
</div>
</div>
<!-- Diagnosis Result -->
<div id="diagnosisCard" class="mt-6 hidden">
<div class="bg-white rounded-xl shadow-lg overflow-hidden">
<div class="bg-gradient-to-r from-green-600 to-green-500 text-white px-4 py-3 flex justify-between items-center">
<h3 class="font-medium">Diagnosis Result</h3>
<div class="flex items-center">
<span class="text-xs bg-white text-green-600 px-2 py-1 rounded-full font-bold">94% Confidence</span>
</div>
</div>
<div class="p-6">
<div class="flex items-start">
<div class="mr-4">
<div class="bg-green-100 text-green-800 rounded-full w-12 h-12 flex items-center justify-center">
<i class="fas fa-check-circle text-2xl"></i>
</div>
</div>
<div>
<h4 class="text-xl font-bold text-gray-800 mb-2">Tumor Detected</h4>
<p class="text-gray-600 mb-4">Our topological analysis identified a mass with characteristics consistent with a <span class="font-bold text-gray-800">meningioma</span>.</p>
<div class="bg-yellow-50 border-l-4 border-yellow-400 p-4">
<div class="flex">
<div class="flex-shrink-0">
<i class="fas fa-exclamation-triangle text-yellow-400"></i>
</div>
<div class="ml-3">
<p class="text-sm text-yellow-700">
<span class="font-bold">Important:</span> This analysis is not a substitute for professional medical diagnosis. Please consult with a neurologist.
</p>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</main>
<!-- How It Works Section -->
<section class="bg-gray-100 py-16">
<div class="container mx-auto px-4">
<h2 class="text-3xl font-bold text-center mb-12">How Our Topological Analysis Works</h2>
<div class="grid grid-cols-1 md:grid-cols-3 gap-8">
<div class="bg-white p-6 rounded-xl shadow-md hover:shadow-lg transition">
<div class="bg-blue-100 w-16 h-16 rounded-full flex items-center justify-center mb-4 mx-auto">
<i class="fas fa-project-diagram text-blue-600 text-2xl"></i>
</div>
<h3 class="text-xl font-bold text-center mb-3">Topological Feature Extraction</h3>
<p class="text-gray-600 text-center">We compute persistence diagrams and Betti numbers to capture the essential shape characteristics of brain structures.</p>
</div>
<div class="bg-white p-6 rounded-xl shadow-md hover:shadow-lg transition">
<div class="bg-purple-100 w-16 h-16 rounded-full flex items-center justify-center mb-4 mx-auto">
<i class="fas fa-brain text-purple-600 text-2xl"></i>
</div>
<h3 class="text-xl font-bold text-center mb-3">Neural Network Analysis</h3>
<p class="text-gray-600 text-center">Our deep learning model processes topological features to identify abnormal patterns indicative of tumors.</p>
</div>
<div class="bg-white p-6 rounded-xl shadow-md hover:shadow-lg transition">
<div class="bg-green-100 w-16 h-16 rounded-full flex items-center justify-center mb-4 mx-auto">
<i class="fas fa-chart-line text-green-600 text-2xl"></i>
</div>
<h3 class="text-xl font-bold text-center mb-3">Confidence Scoring</h3>
<p class="text-gray-600 text-center">The system provides a confidence score based on the persistence of topological features in abnormal regions.</p>
</div>
</div>
</div>
</section>
<section class="bg-gray-100 py-16" id="research">
<div class="container mx-auto px-4">
<h2 class="text-3xl font-bold text-center mb-12">Topological Analysis Mathematics</h2>
<div class="bg-white rounded-xl shadow-lg p-8 mb-8">
<h3 class="text-2xl font-bold mb-4">1. Persistent Homology</h3>
<p class="mb-4">Our system uses algebraic topology to quantify shape characteristics through persistent homology:</p>
<div class="grid grid-cols-1 md:grid-cols-2 gap-8">
<div class="bg-blue-50 p-4 rounded-lg">
<h4 class="font-bold mb-2">Filtration Process</h4>
<p>For image I: Ω → ℝ, we construct filtration:</p>
<p class="text-center my-2 font-mono">
F(α) = {p ∈ Ω | I(p) ≥ α}, α ∈ [0, 255]
</p>
<p>Tracking topological features across scales α</p>
</div>
<div class="bg-green-50 p-4 rounded-lg">
<h4 class="font-bold mb-2">Persistence Diagram</h4>
<p>For each topological feature (component, hole):</p>
<p class="text-center my-2 font-mono">
D(I) = {(b<sub>i</sub>, d<sub>i</sub>) ∈ ℝ² | i ∈ features}
</p>
<p>Where b=birth time, d=death time</p>
</div>
</div>
</div>
<div class="bg-white rounded-xl shadow-lg p-8 mb-8">
<h3 class="text-2xl font-bold mb-4">2. Betti Numbers Analysis</h3>
<p>We compute Betti numbers for tumor characterization:</p>
<div class="overflow-x-auto mt-4">
<table class="min-w-full bg-white">
<thead class="bg-gray-800 text-white">
<tr>
<th class="py-3 px-4">Betti Number</th>
<th class="py-3 px-4">Description</th>
<th class="py-3 px-4">Tumor Significance</th>
</tr>
</thead>
<tbody>
<tr class="border-b">
<td class="py-3 px-4 font-bold">β₀</td>
<td class="py-3 px-4">Connected components</td>
<td class="py-3 px-4">Tumor count/multiplicity</td>
</tr>
<tr class="border-b">
<td class="py-3 px-4 font-bold">β₁</td>
<td class="py-3 px-4">1-dimensional holes</td>
<td class="py-3 px-4">Tumor morphology complexity</td>
</tr>
<tr>
<td class="py-3 px-4 font-bold">β₂</td>
<td class="py-3 px-4">Void spaces</td>
<td class="py-3 px-4">3D tumor structure analysis</td>
</tr>
</tbody>
</table>
</div>
</div>
<div class="bg-white rounded-xl shadow-lg p-8">
<h3 class="text-2xl font-bold mb-4">3. Topological Loss Function</h3>
<p>Our model optimizes using persistent homology-based loss:</p>
<div class="mt-4 space-y-4">
<div class="bg-purple-50 p-4 rounded-lg">
<p class="font-mono text-center">
L<sub>topo</sub> = ∑|D<sub>pred</sub> - D<sub>true</sub>|² + λW(D<sub>pred</sub>, D<sub>true</sub>)
</p>
</div>
<div class="grid grid-cols-1 md:grid-cols-2 gap-4">
<div class="p-4 border rounded-lg">
<h4 class="font-bold mb-2">Wasserstein Distance</h4>
<p>Measures similarity between persistence diagrams:</p>
<p class="mt-2 font-mono">
W(D₁,D₂) = inf<sub>η</sub> ∑||x - η(x)||<sub>p</sub>
</p>
</div>
<div class="p-4 border rounded-lg">
<h4 class="font-bold mb-2">Euler Characteristic</h4>
<p>Alternating sum of Betti numbers:</p>
<p class="mt-2 font-mono">
χ = β₀ - β₁ + β₂
</p>
</div>
</div>
</div>
</div>
</div>
</section>
<!-- Footer -->
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<p class="text-gray-400">Advanced topological analysis for early brain tumor detection.</p>
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<p>© 2025 NeuroScan. All rights reserved.</p>
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originalImage.src = 'https://upload.wikimedia.org/wikipedia/commons/thumb/5/5d/Axial_brain_MRI.jpg/640px-Axial_brain_MRI.jpg';
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