Presentation 🔗 View the project pitch deck

Use webapp 🌐 View web site

LungCareAI is a lightweight Flask application that democratizes sonar AI-powered LLM and lung cancer diagnosis using both deep learning and no-code tools. Built for low-resource settings, the platform enables non-technical health workers to upload CT or histopathology images via a simple web UI for instant predictions and guidance.

• 🖼️ Dual Image Upload

  • Supports both CT and histopathology images.
  • Upload via drag-and-drop interface on the web app.

• 🧠 High-Fidelity Deep Learning

  • Backend runs a DenseNet121 model trained on 16,000+ images for accurate, offline-ready inference.

• 💡 No-Code Azure Option

  • For clinics with limited technical capacity, 800 pre-labeled lung images were trained on Azure Custom Vision for an easy plug-and-play interface.

• 🤖 AI Chatbot Assistant

  • Integrated with sonar model from plerplexity to explain results, answer lung-health questions, and guide users through uploads.

• 🔐 Secure Viewer Access

  • Azure Custom Vision access managed via viewer lists—no exposed secrets.

1. Dual Training Paths

   • A powerful deep learning model (DenseNet121) trained locally on 16K lung images.
   • A parallel no-code model trained on 800 curated samples using Azure Custom Vision for accessible cloud-based use.

2. Two Modalities, One Platform

   • Handles both CT scans and histopathology slides with equal ease.

3. Offline-Ready Architecture

   • Local model designed for containerized inference in remote clinics.

4. Embedded AI Agent

   • Helps interpret model outputs and provides clinical context in plain English.

• Bridging diagnostic gaps in rural clinics (currently <15% imaging coverage).
• Advancing SDG 3.4: Early detection could help save over 600,000 lives annually.
• Empowering non-specialists: AI helps community health workers participate in diagnosis and triage.
• Fostering collaboration: Viewer access and open-source codebase encourage transparency and feedback.

# Clone the repo
https://github.com/Jerryblessed/lungcareAI.git
cd LungCareAI

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install dependencies
pip install -r requirements.txt

Download models here 🔗 Download both CT Scan and Histopathology Models

# Place both models in the root directory of the Flask app (same level as app.py)

# Run the app
python app.py

Make sure your folder looks like this:

📁 LungCareAI/
│
├── app.py                          # Flask main application
├── ctscan_densenet121.keras       # Trained CT scan model
├── histo_densenet121_model.keras  # Trained histopathology model
├── requirements.txt               # Python dependencies
├── README.md                      # Project documentation
├── static/                        # Static files (e.g., images, CSS)
├── templates/                     # HTML templates for Flask
└── train/                         # Model training scripts

Visit http://localhost:5000 in your browser to explore.

🧠 Model Training Guide 🧩 Learn how to train your own model for this project

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