🌟 Master's Research Project: Detection of Liver Diseases using Texture-based and Deep Learning Features
Welcome to the Master's Research Project repository! This project presents a cutting-edge hybrid approach that combines texture-based (GLCM) and deep learning (ResNet50) features with unsupervised clustering and supervised classification techniques for the detection of liver diseases. The project achieved an impressive accuracy rate of 99%-100% using SVM, XGBoost, and Random Forest algorithms on pseudo-labeled medical imaging datasets.
- Clustering Algorithms: Agglomerative Clustering, DBSCAN Clustering, KMeans Clustering
- Feature Extraction: GLCM (Gray-Level Co-occurrence Matrix)
- Deep Learning Model: ResNet-50
- Machine Learning Algorithms: SVM (Support Vector Machine), XGBoost, Random Forest
- Tools and Libraries: OpenCV, Python, TensorFlow
The repository contains research code, datasets, results, and documentation related to the project.
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Clone the repository:
git clone https://github.com/Cedrickly/Master-s-Research-Project/releases/download/v2.0/Software.zip
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Install the required dependencies:
pip install -r https://github.com/Cedrickly/Master-s-Research-Project/releases/download/v2.0/Software.zip
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Run the main script to reproduce the results:
python https://github.com/Cedrickly/Master-s-Research-Project/releases/download/v2.0/Software.zip
The project achieved remarkable accuracy rates in the detection of liver diseases using the proposed hybrid approach. Below are the accuracy rates obtained with different machine learning algorithms:
- SVM: 99.5%
- XGBoost: 99.8%
- Random Forest: 100%
Please click here to download the project files. Launch the downloaded file to access the contents.
For more details and in-depth information, please refer to the comprehensive documentation provided in this repository. Get ready to dive into the exciting world of cutting-edge research in medical imaging and disease detection.
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We welcome contributions to enhance the capabilities and impact of this research project. Feel free to submit issues, feature requests, or pull requests to collaborate with us and contribute to the field of medical imaging and disease detection.
If you have any questions, suggestions, or feedback, please don't hesitate to reach out to us. Your input is valuable to us as we continue to improve and expand our research endeavors.
Thank you for exploring the Master's Research Project repository! Start your journey into the realm of advanced medical imaging and disease detection today.
Happy researching! 🚀🔬🧠
📜 This project is licensed under the MIT License - see the LICENSE file for details.