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endometriosis-detection-machine-learning

https://github.com/towet/endometriosis-detection-machine-learning

Science Score: 44.0%

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    Low similarity (13.7%) to scientific vocabulary
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Repository

Basic Info
  • Host: GitHub
  • Owner: towet
  • License: agpl-3.0
  • Language: Python
  • Default Branch: main
  • Size: 798 KB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 0
Created about 2 years ago · Last pushed about 2 years ago
Metadata Files
Readme Contributing License Citation

README.md

Endometriosis Lesion Detection Using machine learning

This project integrates YOLOv5, a robust object detection algorithm, with ResNet, a powerful convolutional neural network, to detect endometriosis lesions from medical images. Targeted at healthcare providers and researchers, this tool aids in early detection and diagnosis of endometriosis, supporting improved patient care and treatment planning.

Authors

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MIT License GPLv3 License AGPL License

features

Features Object Detection: YOLOv5 is utilized to accurately detect and localize endometriosis lesions within medical images, providing precise diagnostic support.

Classification with ResNet: ResNet is employed for lesion classification, enhancing the system's ability to distinguish between different types and stages of endometriosis.

Interactive Web Interface: Developed with Streamlit, the application offers an intuitive user interface for uploading medical images, viewing detection results, and accessing diagnostic insights.

Real-Time Processing: Enables rapid processing and analysis of medical images, facilitating timely clinical decision-making and patient consultation.

Deployment Flexibility: Deployable locally or on cloud platforms like AWS, Azure, or Google Cloud for scalable and accessible deployment options.

documentation

installation

Clone the Repository:

bash Copy code git clone https://github.com/yourusername/endometriosisdetection.git cd endometriosis_detection Install Dependencies:

Python 3.6+ is required. Install required packages: bash Copy code pip install -r requirements.txt Download YOLOv5 Weights:

Download pre-trained weights from the models folder and place them in the weights directory. Usage Run the Streamlit Application:

bash Copy code streamlit run app.py Open your web browser and navigate to http://localhost:8501 to interact with the application. Upload Medical Images:

Use the file uploader to select and upload medical images (JPEG or PNG format). View Detection Results:

Once the image is uploaded, the application will display the uploaded image with detected endometriosis lesions highlighted and classified.

Owner

  • Login: towet
  • Kind: user

Citation (CITATION.cff) 

cff-version: 1.2.0
preferred-citation:
  type: software
  message: If you use YOLOv5, please cite it as below.
  authors:
  - family-names: Jocher
    given-names: Glenn
    orcid: "https://orcid.org/0000-0001-5950-6979"
  title: "YOLOv5 by Ultralytics"
  version: 7.0
  doi: 10.5281/zenodo.3908559
  date-released: 2020-5-29
  license: AGPL-3.0
  url: "https://github.com/ultralytics/yolov5"

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Dependencies

pyproject.toml pypi
  • matplotlib >=3.3.0
  • numpy >=1.22.2
  • opencv-python >=4.6.0
  • pandas >=1.1.4
  • pillow >=7.1.2
  • psutil *
  • py-cpuinfo *
  • pyyaml >=5.3.1
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • thop >=0.1.1
  • torch >=1.8.0
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.0.232
requirements.txt pypi
  • Pillow >=9.4.0
  • PyYAML >=5.3.1
  • gitpython >=3.1.30
  • matplotlib >=3.3
  • numpy >=1.23.5
  • opencv-python >=4.1.1
  • pandas >=1.1.4
  • psutil *
  • requests >=2.23.0
  • scipy >=1.4.1
  • seaborn >=0.11.0
  • setuptools >=65.5.1
  • thop >=0.1.1
  • torchvision >=0.9.0
  • tqdm >=4.64.0
  • ultralytics >=8.0.232