vidhi1290 / medical-image-segmentation-deep-learning-project

Our project uses state-of-the-art deep learning techniques to tackle a vital medical task: polyp segmentation from colonoscopy images. We harness the Unet++ architecture and a robust tech stack to precisely detect and isolate polyps, advancing healthcare diagnostics and patient care. 🏥💡

Polyp Segmentation using Unet++

🔬 Explore Medical Image Segmentation with Deep Learning

In healthcare and medical science, the fusion of artificial intelligence and deep learning is revolutionizing diagnostics. My project focuses on the precise segmentation of polyps from colonoscopy images—a vital tool for medical practitioners.

📁 Data

The use of the CVC-Clinic database, containing frames from colonoscopy videos. The dataset includes polyp frames and corresponding ground truth images in both PNG and TIFF formats.

🎯 Aim

I aimed to develop a robust polyp segmentation model using deep learning techniques.

💻 Tech Stack

• Language: Python
• Deep Learning: PyTorch
• Computer Vision: OpenCV
• Libraries: Scikit-learn, Pandas, NumPy, Albumentations, and more.

🚀 Approach

1. Data Insight: Understand the dataset.
2. Evaluation Metrics: Grasp the evaluation criteria.
3. Unet Architecture: Explore Unet for medical applications.
4. Unet++ Advantage: Discover the benefits of Unet++.
5. Environment Setup: Get your project environment ready.
6. Data Augmentation: Enhance data for better performance.
7. Model Building: Create the Unet++ model with PyTorch.
8. Model Training: Train the model (GPU recommended).
9. Model Prediction: Understand the modular code structure.

📂 Project Structure

1. input: Contains data (PNG and TIFF folders).
2. src: The heart of the project with modular code, including ML pipelines, engine, and config.
3. output: Stores trained models and predictions.
4. lib: Reference materials (original iPython notebook).
5. requirements.txt: Lists all dependencies.

🎓 Takeaways

• Polyp Segmentation Insights
• IOU Metric Understanding
• Data Augmentation Techniques
• Practical PyTorch Data Augmentation
• Medical Computer Vision Applications
• Building CNN Models
• OpenCV for Computer Vision
• VGG Architecture Familiarity
• Unet and Unet++ Knowledge
• Building VGG Blocks with PyTorch
• Training Unet++ Models

Contact Information

For questions, collaborations, or further information, feel free to contact me:

• Project Reference: Project Pro
• LinkedIn: LinkedIn Profile
• Email: [email protected]

🚀 Getting Started

# Clone the repository
git clone https://github.com/YourUsername/YourRepository.git

# Navigate to the project directory
cd Medical-Image-Segmentation-Deep-Learning-Project

# Install dependencies
pip install -r requirements.txt