🫁 Chest Cancer Classification using Deep Learning

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📌 Overview

An end-to-end deep learning project for detecting Adenocarcinoma cancer from chest CT scan images. The project is designed with production-grade MLOps practices, covering everything from data ingestion and training to deployment with automated CI/CD pipelines.

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✨ Key Features

🧠 Machine Learning

• Transfer Learning using EfficientNetB0
• Modular, reusable training pipeline
• MLflow for experiment tracking & model versioning
• DVC for reproducible data and pipeline management

🚀 Production Application

• FastAPI-based REST API
• Simple web UI with image upload support
• Model lazy loading & caching for fast inference
• Health-check endpoint for monitoring

⚙️ MLOps & DevOps

• CI/CD pipelines using GitHub Actions
• Dockerized application for consistent deployment
• AWS ECS ready deployment workflow
• Environment-based configuration for secrets

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🛠️ Tech Stack

Core ML / DL

• TensorFlow & Keras
• EfficientNetB0
• NumPy, Pandas

MLOps

• MLflow
• DVC
• DagHub

Backend & API

• FastAPI
• Uvicorn
• Python-multipart

Frontend

• HTML + TailwindCSS
• Vanilla JavaScript

DevOps & Cloud

• Docker
• GitHub Actions
• AWS ECS & ECR

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📁 Project Structure

├── .github/
│   └── workflows/
│       └── main.yaml              # CI/CD pipeline
├── artifacts/
│   ├── data_ingestion/            # Raw & processed data
│   ├── prepare_base_model/        # Base & updated models
│   └── training/                  # Trained models & logs
├── config/
│   └── config.yaml                # Central configuration
├── src/cnnClassifier/
│   ├── components/                # Core ML components
│   ├── pipeline/                  # Training & inference pipelines
│   ├── config/                    # Configuration manager
│   ├── entity/                    # Dataclasses
│   ├── utils/                     # Utility helpers
│   └── constants/
├── templates/
│   └── index.html                 # Web UI
├── app.py                         # FastAPI app
├── main.py                        # Training pipeline entry
├── dvc.yaml                       # DVC pipeline
├── params.yaml                    # Model parameters
├── requirements.txt
├── Dockerfile
├── .dockerignore
└── README.md

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🚀 Getting Started

Prerequisites

• Python 3.10+
• Docker (optional)
• AWS CLI (for cloud deployment)

Local Setup

1. Clone the repository

   git clone https://github.com/CodeBy-HP/Chest-Cancer-Classification.git
   cd Chest-Cancer-Classification

2. Create & activate virtual environment

   python -m venv venv
   source venv/bin/activate  # Linux / macOS
   # venv\\Scripts\\activate  # Windows

3. Install dependencies

   pip install -r requirements.txt

4. Configure environment variables

   cp .env.example .env
   # update credentials inside .env

5. Run the application

   python app.py

   Visit: http://localhost:8000

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🐳 Docker Setup

# Build image
docker build -t chest-cancer-classifier .

# Run container
docker run -p 8000:8000 --env-file .env chest-cancer-classifier

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📊 Training Pipeline

# Run full training pipeline
python main.py

# Or via DVC
dvc repro

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