parkinson-detector 0.0.1

An Ensemble of CNN Models for Parkinsons Disease Detection

Parkinson detector

An Ensemble of CNN Models for Parkinson’s Disease Detection

Medical support tool for fast preliminary diagnosis can be used by any medical personnel. Parkinson detector is written in Python and runs in a normal Windows and Linux environment. The user interface was implemented using the Qt library.

Our application can work directly with Dicom files (.dcm) from a digital CRT machine or with any image files (jpg, png, etc.). We make a simple user interface with drag-and-drop support.

Note: prediction from the application cannot be used as a medical diagnosis.

Application requirements:

• Operational system:
  • Windows 7 or later
  • Ubuntu 16.04 or later
  • Mac OS 10.12.6 (Sierra) or later (64-bit) (no GPU support)
• Python 3.6 or later
• Hard Drive: 4Gb of free space,
• Processor: Intel Core i3,
• Memory (RAM): 3Gb or above free.
• Internet connection: wideband connection for first use (for neural network model downloading)
• Admin privileges are not a requirement

Run without instalation

Requirements instalation

git clone https://gitlab.com/digiratory/biomedimaging/parkinson-detector.git
cd parkinson-detector
pip install -r requirements.txt

Application starting

cd parkinson-detector
python run.pyw

Instalation over pip

pip install parkinson-detector

For starting application run the follow command:

parkinson-detector-app

Citation

If you find this project useful, please cite Kurmi A, Biswas S, Sen S, Sinitca A, Kaplun D, Sarkar R. An Ensemble of CNN Models for Parkinson’s Disease Detection Using DaTscan Images. Diagnostics. 2022; 12(5):1173. https://doi.org/10.3390/diagnostics12051173 :

@Article{diagnostics12051173,
    AUTHOR = {Kurmi, Ankit and Biswas, Shreya and Sen, Shibaprasad and Sinitca, Aleksandr and Kaplun, Dmitrii and Sarkar, Ram},
    TITLE = {An Ensemble of CNN Models for Parkinson’s Disease Detection Using DaTscan Images},
    JOURNAL = {Diagnostics},
    VOLUME = {12},
    YEAR = {2022},
    NUMBER = {5},
    ARTICLE-NUMBER = {1173},
    URL = {https://www.mdpi.com/2075-4418/12/5/1173},
    ISSN = {2075-4418},
    DOI = {10.3390/diagnostics12051173}
}