Projects

Name: AI-Assisted Lung Cancer Detection System
Author: Govind Madhav

Things I have built — from full-stack apps to small experiments.

SYSTEM ARCHITECTURE DIAGRAMInteractive Flowchart
Rendering Architecture Diagram...

lung_cancer_project/
├── backend/                  # FastAPI & PyTorch Backend
│   ├── app/
│   │   ├── api/             # REST Endpoints (predict, health, metrics, patients)
│   │   ├── core/            # Config, Security, Exception Handlers
│   │   ├── db/              # SQLAlchemy Models & DB Session Management
│   │   ├── services/        # Inference Engine & Grad-CAM Services
│   │   └── main.py          # FastAPI App Entrypoint
│   ├── ml_train/            # Model Training Scripts & Notebooks
│   ├── models/              # Pretrained PyTorch Weights (.pth / .onnx)
│   ├── Dockerfile           # Backend Container Specification
│   ├── requirements.txt     # Python Dependencies
│   └── setup_mysql.py       # Database Initialization Utility
│
├── frontend/                 # React 18 + TypeScript Frontend
│   ├── src/
│   │   ├── pages/           # Home, Predict, Result, Metrics, Ethics
│   │   ├── components/      # Medical UI components (ResultCard, ExplainabilityViewer)
│   │   ├── services/        # Axios API Client & Prediction Services
│   │   ├── hooks/           # Custom React Hooks (usePrediction, useDisclaimer)
│   │   └── schemas/         # TypeScript API Data Contracts
│   ├── package.json         # Node Dependencies
│   ├── tailwind.config.js   # Custom Clinical Color Palette
│   └── vite.config.ts       # Vite Build Configuration
│
└── README.md                 # Project Documentation

# Navigate to backend directory
cd backend

# Create and activate virtual environment
python -m venv .venv
# On Windows:
.venv\Scripts\activate
# On Linux/macOS:
source .venv/bin/activate

# Install dependencies
pip install -r requirements.txt

# Run database setup (SQLite / MySQL)
python setup_mysql.py

# Start the FastAPI development server
uvicorn app.main:app --reload --port 8000

# Open a new terminal and navigate to frontend directory
cd frontend

# Install Node packages
npm install

# Start Vite dev server
npm run dev

# Build backend image
docker build -t lung-cancer-backend ./backend

# Run container
docker run -d -p 8000:8000 --name lung_cancer_api lung-cancer-backend

Endpoint	Method	Description
`POST /api/v1/predict`	`POST`	Upload CT scan file to receive prediction status, risk level, confidence scores, and Grad-CAM heatmap.
`GET /api/v1/health`	`GET`	API health check and DB connection verification.
`GET /api/v1/metrics`	`GET`	Fetch model performance evaluation metrics (Sensitivity, Specificity, AUC-ROC).
`GET /api/v1/patients/`	`GET`	Retrieve anonymized patient reference logs.

Projects

AI-Assisted Lung Cancer Detection System

🫁 Multi-Model AI Lung Cancer Detection System

🌟 Key Features

🧠 AI & Backend Capabilities

⚕️ Clinical UX & Safety Design

🏗️ System Architecture

📂 Project Structure

🚀 Getting Started

Prerequisites

1. Backend Setup

2. Frontend Setup

3. Docker Deployment (Optional)

🔌 API Reference Highlights

🛡️ Medical Ethics & Clinical Safety Disclosures

📄 License