AI-Powered Chest X-Ray Lung Cancer Detection System with Local Web Server

In this project, I built LungAI, an AI-powered lung cancer detection system that uses Deep Learning and image processing to classify chest X-ray images as normal or cancerous. This project combines a pre-trained VGG16 model with a custom-trained classifier to achieve accurate results. The system runs entirely on a local Flask web server, allowing users to easily upload X-ray images, receive instant predictions, and view motivational messages along with a clear model accuracy chart.

Built with Python, TensorFlow, Keras, OpenCV, Flask, HTML, CSS, and Bootstrap, LungAI is designed to be simple yet powerful — ideal for college projects, research, or demonstration purposes. The user interface is colorful, mobile-friendly, and includes dynamic messages to keep users positive, whether the prediction is normal or indicates a possible issue.

This post explains how I trained the model, how I built the web server, and how you can replicate or extend this project for other medical applications. For full code, step-by-step guide, and download links, check the links below!

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✅ Key Points to Include After Intro (for the blog body)

✔️ Problem statement: Early detection of lung cancer saves lives.
✔️ Dataset: Chest X-ray images split into Train/Test/Validation.
✔️ AI Model: VGG16 Transfer Learning, fine-tuned for X-ray classification.
✔️ Technologies: Python, TensorFlow, OpenCV, Flask, Bootstrap.
✔️ How it works: Upload, predict, get result + motivational line.
✔️ Extra: Accuracy chart shows model performance clearly.
✔️ Deployment: Runs locally with simple python app.py.
✔️ Source code & contact: Instagram, WhatsApp, portfolio link.