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https://github.com/bniladridas/lung-nodule
Cutting-edge AI for Healthcare
https://github.com/bniladridas/lung-nodule
artificial-intelligence cnn deep-learning jupyter-notebook
Last synced: about 2 months ago
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Cutting-edge AI for Healthcare
- Host: GitHub
- URL: https://github.com/bniladridas/lung-nodule
- Owner: bniladridas
- License: mit
- Created: 2024-09-09T07:30:14.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2024-09-09T07:35:22.000Z (4 months ago)
- Last Synced: 2024-10-20T05:40:51.568Z (3 months ago)
- Topics: artificial-intelligence, cnn, deep-learning, jupyter-notebook
- Language: Python
- Homepage:
- Size: 13.7 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# LungScan-AhR
**Transforming Lung Cancer Detection with AI**
Welcome to **LungScan-AhR**, an innovative project dedicated to developing a cutting-edge, non-invasive AI-powered system for detecting lung cancer linked to combustion particles and fine particulate matter (PM2.5). Our mission is to leverage advanced machine learning techniques to enhance early detection and improve patient outcomes.
## 🚀 Project Scope
- **Machine Learning Model Development**: Build a robust model to analyze CT scans, identifying patterns indicative of lung cancer.
- **Research**: Explore the impact of polycyclic aromatic hydrocarbons (PAHs) and the aryl hydrocarbon receptor (AhR) on lung cancer progression.
## 🌟 Getting Started
1. **Clone the Repository**:
```bash
git clone [email protected]:niladridas/LungScan-AhR.git
```
2. **Install Dependencies**:
```bash
pip install -r requirements.txt
```
3. **Run the Project**:
```bash
python3 main.py
```
## 🛠️ Technologies and Tools
- **Jupyter Notebook**: Our primary development environment for data science tasks including exploration, preprocessing, and model evaluation.
- **Python**: The core programming language for scripting, data manipulation, and AI model development.
- **Custom Datasets**: Tailored datasets generated through Python scripts for training and evaluating our models.
## 🔬 Development Process
1. **Data Generation**: Create custom datasets with Python for accurate training and evaluation.
2. **Data Exploration**: Utilize Jupyter Notebook for in-depth data analysis and visualization.
3. **Data Preprocessing**: Apply techniques like cleaning, transformation, and normalization to ready data for model training.
4. **Model Training**: Implement supervised, unsupervised, and reinforcement learning techniques.
5. **Model Evaluation**: Assess model performance with metrics such as accuracy, precision, recall, F1-score, and ROC-AUC.
6. **Model Refining**: Continuously refine models based on evaluation feedback for optimal performance.
## 🔍 Methodologies
- **Data-Driven Approach**: Harness machine learning and deep learning to detect lung cancer associated with combustion particles and PM2.5.
- **Iterative Development**: Engage in cycles of data generation, exploration, preprocessing, training, evaluation, and refinement.
- **Collaborative Effort**: Collaborate with data scientists, AI engineers, and medical experts to ensure system efficacy and accuracy.
## 📜 License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.