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<html lang="en" data-theme="light">
<head>
<meta charset="UTF-8" />
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<title>KidneyDL CT Scan Classifier</title>
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</head>
<body>
<nav class="topbar">
<div class="topbar-brand">
<div class="pulse"></div>
KidneyDL
</div>
<button class="theme-btn" id="themeBtn" onclick="toggleTheme()">
<svg id="themeIco" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
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</svg>
<span id="themeLabel">Light mode</span>
</button>
</nav>
<div class="page">
<!-- Hero -->
<div class="hero">
<div class="hero-badge"><div class="dot"></div> AI Powered Medical Imaging</div>
<h1>Kidney CT Scan<br/>Tumor Classifier</h1>
<p>
A deep learning system built to help detect kidney tumors from CT scan images.
Upload a scan and the model will tell you within seconds whether the kidney
appears normal or shows signs of a tumor. Built with transfer learning,
full experiment tracking, and a reproducible MLOps pipeline.
</p>
</div>
<!-- Classifier -->
<div class="card">
<div class="section-eyebrow">Upload a CT Scan Image</div>
<div class="classifier-grid">
<div>
<div class="drop-zone" id="dropZone" onclick="document.getElementById('fileInput').click()">
<div class="dz-icon">
<!-- Kidney bean icon: convex lateral side, concave medial (hilum) side -->
<svg width="52" height="64" viewBox="0 0 52 64" fill="none" stroke="currentColor"
stroke-width="3" stroke-linecap="round" stroke-linejoin="round"
style="opacity:0.55;display:block;margin:0 auto 4px">
<path d="M26 4 C41 4 49 15 49 29 C49 46 41 60 26 61
C15 60 7 54 5 45 C3 38 5 31 9 28
C12 25 12 22 9 18 C6 14 10 4 26 4 Z"/>
<path d="M12 29 C15 24 15 18 12 13" stroke-width="2.2"/>
</svg>
</div>
<p class="dz-hint">
Drop your CT scan image here<br/>
or <b>click to choose a file</b>
</p>
<input type="file" id="fileInput" accept="image/*" />
</div>
</div>
<div class="preview-box" id="previewBox">
<div class="preview-empty" id="previewEmpty">
<svg viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="1.2">
<rect x="3" y="3" width="18" height="18" rx="2"/>
<circle cx="8.5" cy="8.5" r="1.5"/>
<polyline points="21 15 16 10 5 21"/>
</svg>
<span>Scan preview will appear here</span>
</div>
<img id="previewImg" alt="CT scan preview" />
<div class="preview-label" id="previewLabel"></div>
</div>
</div>
<div class="btn-row">
<button class="btn btn-primary" id="predictBtn" onclick="predict()" disabled>
<svg width="15" height="15" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2.5" stroke-linecap="round" stroke-linejoin="round">
<circle cx="11" cy="11" r="8"/><path d="m21 21-4.35-4.35"/>
</svg>
Analyse Scan
</button>
<button class="btn btn-ghost" id="trainBtn" onclick="trainModel()">
<svg width="15" height="15" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
<polyline points="23 4 23 10 17 10"/>
<path d="M20.49 15a9 9 0 1 1-2.12-9.36L23 10"/>
</svg>
Retrain
</button>
</div>
<div id="loading">
<div class="ring"></div>
<span>Analysing your scan with AI, please wait...</span>
</div>
<div id="result">
<div class="res-row">
<div class="res-ico" id="resIco"></div>
<div>
<div class="res-title" id="resTitle"></div>
<div class="res-sub" id="resSub"></div>
</div>
</div>
<div class="conf-wrap">
<div class="conf-meta">
<span>Model Confidence</span>
<span id="confPct"></span>
</div>
<div class="conf-track">
<div class="conf-fill" id="confFill" style="width:0%"></div>
</div>
</div>
</div>
<div class="disclaimer">
<strong>Important notice:</strong> This tool is intended for research and educational use only.
It is not a certified medical device and should never replace the judgement of a qualified
radiologist or physician. Please seek professional medical advice for any health concerns.
</div>
</div>
<!-- About the project -->
<div class="divider">About the Project</div>
<div class="info-grid">
<div class="info-card">
<div class="ico-wrap ic-blue">🧠</div>
<h3>Why VGG16?</h3>
<p>
VGG16 was chosen because its deep stack of simple 3x3 convolution layers is
remarkably good at learning fine-grained textures, which is exactly what you need
when distinguishing healthy renal tissue from abnormal cell growth in a CT scan.
Pre-trained on ImageNet, its weights already encode a rich understanding of edges,
shapes, and spatial patterns, making it an ideal starting point for medical imaging
tasks where labelled data is limited.
</p>
</div>
<div class="info-card">
<div class="ico-wrap ic-violet">📊</div>
<h3>How the Model Was Built</h3>
<p>
The training process used transfer learning. The VGG16 base layers were frozen
to preserve the knowledge captured from ImageNet, and a custom classification
head was added and fine-tuned on kidney CT scan images split 70 percent for
training and 30 percent for validation. Every experiment was tracked end to end
with MLflow on DagsHub, capturing parameters, metrics, and model artifacts for
full auditability and comparison across runs.
</p>
</div>
<div class="info-card">
<div class="ico-wrap ic-teal">⚙️</div>
<h3>MLOps Pipeline</h3>
<p>
The project is structured around four fully automated DVC pipeline stages:
data ingestion, base model preparation, training, and evaluation.
Each stage is versioned independently so that only what has changed is
re-executed on the next run. Model metrics are pushed automatically to the
MLflow registry, enabling side-by-side comparison of runs and straightforward
model promotion to production.
</p>
</div>
<div class="info-card">
<div class="ico-wrap ic-amber">🧰</div>
<h3>Tech Stack</h3>
<p>Built with tools that are standard in modern ML engineering teams.</p>
<div class="badges">
<span class="badge">🐍 Python 3.13</span>
<span class="badge">🧮 TensorFlow and Keras</span>
<span class="badge">📈 MLflow</span>
<span class="badge">💾 DVC</span>
<span class="badge">🌊 DagsHub</span>
<span class="badge">🛠️ Flask</span>
<span class="badge">🐳 Docker</span>
<span class="badge">📸 VGG16</span>
</div>
</div>
</div>
<!-- Author -->
<div class="divider">About the Author</div>
<div class="author">
<div class="avatar">PS</div>
<div>
<div class="author-name">Paul Sentongo</div>
<div class="author-title">Data Science Researcher | MSc Data Science | Open to New Opportunities</div>
<p class="author-bio">
Paul is a data scientist and applied AI researcher with a Master's degree in Data Science,
driven by a genuine curiosity about how machine learning can be applied to problems that
actually matter in healthcare, sustainability, and social impact.
<br/><br/>
His work sits at the intersection of deep learning, computer vision, and production-ready
MLOps infrastructure. He brings both the academic rigour to understand what is happening
under the hood of a model and the engineering discipline to build systems that work
reliably in the real world. This project is one example of that thinking: not just
training a model, but building the entire scaffold around it so that experiments are
reproducible, results are traceable, and the system can be handed off to anyone and
still run cleanly.
<br/><br/>
Paul is currently looking for research or industry roles where he can contribute to
meaningful AI work, grow alongside talented teams, and keep building things worth building.
</p>
<div class="author-links">
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C.792 0 0 .774 0 1.729v20.542C0 23.227.792 24 1.771 24h20.451C23.2 24 24 23.227
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LinkedIn
</a>
</div>
</div>
</div>
<div class="footer">
Built with care by <strong>Paul Sentongo</strong> |
VGG16 Transfer Learning | Flask | DVC | MLflow
<br/><br/>
© 2025 KidneyDL | Research Project
</div>
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