Biomedical Image Analysis & Disease Detection Suite

Welcome to the Biomedical Image Analysis & Disease Detection Suite repository! This repository is a comprehensive collection of machine learning and deep learning projects focused on biomedical image analysis, disease detection, and healthcare analytics.

🌟 Overview

This suite showcases advanced AI applications in healthcare, featuring state-of-the-art computer vision and machine learning techniques for medical image analysis and disease prediction. The repository is organized into specialized folders for better navigation and focus.

📂 Repository Structure

This repository is organized into specialized folders:

🏥 Medical-Projects/

Contains biomedical imaging and disease detection projects:

• Histopathologic Cancer Detection using CNNs: CNN-based detection of metastatic cancer in lymph node scans
• Lung Cancer Analysis & Accuracy 96.4%: Multi-model analysis of lung cancer data
• Skin Cancer Detection (97.88% accuracy): High-accuracy skin cancer classification
• TAP HCD: Advanced histopathologic cancer detection with CBAM attention mechanisms

📊 Other-Projects/

Additional machine learning applications:

• zomato_review_analysis_using_NLP_project.ipynb: Sentiment analysis on restaurant reviews
• Trent Prediction analysis.ipynb: Financial forecasting and investment analysis

🚀 Getting Started

To explore these projects, you can clone the repository and install the necessary dependencies.

1. Clone the repository:

   git clone https://github.com/Devguru-codes/Extra-Projects-ML.git

   (Replace YourUsername with your actual GitHub username)

2. Navigate to the directory:

   cd Extra-Projects-ML

3. Install dependencies: It is highly recommended to use a virtual environment. The dependencies may vary slightly between projects, but you can start by installing the most common libraries:

   pip install numpy pandas matplotlib seaborn scikit-learn tensorflow keras opencv-python jupyter notebook

   For specific projects, you might need to install other libraries. Check the individual notebooks for any special requirements.

4. Launch Jupyter Notebook:

   jupyter notebook

   This will open a new tab in your web browser, where you can navigate to and open any of the project notebooks (.ipynb files).

🤝 Contributing

Contributions, issues, and feature requests are welcome! Feel free to check the issues page if you want to contribute.

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/NewProject)
3. Commit your Changes (git commit -m 'Add some NewProject')
4. Push to the Branch (git push origin feature/NewProject)
5. Open a Pull Request

Enjoy exploring the projects!