Lung Cancer Detection using Convolutional Neural Network (CNN) V2

This project demonstrates how to use Convolutional Neural Networks (CNNs) for the automatic detection of lung cancer from medical images. The implementation is provided in a Jupyter notebook and leverages deep learning methods to classify images and aid in the early detection of lung cancer.

Table of Contents

• Project Overview
• Dataset
• Model Architecture
• Results
• Installation
• Usage
• Future Enhancements
• Contributing
• License

Project Overview

Early detection of lung cancer can significantly improve treatment outcomes. This notebook presents a deep learning approach using CNNs to classify lung images as cancerous or non-cancerous. The workflow includes data preprocessing, model training, evaluation, and prediction.

Dataset

• The dataset used consists of labeled lung images for training and testing.
• [Insert dataset source or location, e.g., Kaggle link]
• Images are preprocessed for normalization and augmentation.

Model Architecture

• Built with TensorFlow/Keras (or specify framework used).
• The CNN consists of multiple convolutional, pooling, and dense layers.
• Batch normalization, dropout, and data augmentation techniques are applied.

Results

• Accuracy and loss curves are provided in the notebook.
• [Insert F1-score, ROC-AUC, confusion matrix or other metrics]
• Example predictions and misclassified images are discussed.

Installation

1. Clone the repository:

   git clone https://github.com/mohalkarushikesh/AI-ML-Engineer-Notes.git

2. Install dependencies:

   pip install -r requirements.txt

   Or use Jupyter's built-in package manager for individual libraries.

Usage

1. Open the Jupyter notebook:

   jupyter notebook Lung_Cancer_Detection_using_Convolutional_Neural_Network\ V2.ipynb

2. Run each cell in order to train and evaluate the model.
3. Customize the notebook for your own dataset or model improvements.

Future Enhancements

• Dataset Expansion: Incorporate larger and more diverse datasets for improved generalization.
• Model Optimization: Experiment with advanced architectures (ResNet, DenseNet, etc.) for better accuracy.
• Explainability: Integrate explainable AI methods (e.g., Grad-CAM, LIME) to visualize model decision-making.
• Transfer Learning: Use pre-trained models to leverage existing knowledge and boost performance.
• Clinical Integration: Develop a user-friendly interface for clinicians to use the model in real-world settings.
• Automation: Automate hyperparameter tuning (using tools like Optuna or Hyperopt).
• Cross-validation: Implement k-fold cross-validation for more robust evaluation.
• Deployment: Package the solution as a web or mobile app for easy access.

Contributing

Contributions are welcome! Please open issues or submit pull requests for bug fixes, improvements, or new features.

License

[Specify your project's license here, e.g., MIT License]