Research Data Leeds Repository

Lung ultrasound COVID phantom dataset used for training machine learning model

McLaughlan, James and Howell, Lewis and Ingram, Nicola (2024) Lung ultrasound COVID phantom dataset used for training machine learning model. University of Leeds. [Dataset] https://doi.org/10.5518/1485

Dataset description

This data repository contains all the raw imaging videos taken on a COVID-19 lung ultrasound phantom with a range of diagnostic ultrasound systems outlined in the methods section of the accompanying paper. These videos were used to generate a bank of imaging frames with a wide variety of COVID-19 features from the ultrasound phantom. MatLab code used to do this is also included in the repository. This data was used to train the included machine learning model, along with the labelled training and test datasets.

Keywords:	machine learning, COVID-19, Lung Ultrasound
Subjects:	B000 - Subjects allied to medicine > B800 - Medical technology H000 - Engineering > H100 - General engineering > H160 - Bioengineering, biomedical engineering & clinical engineering > H166 - Imaging I000 - Computer sciences > I400 - Artificial intelligence I000 - Computer sciences > I400 - Artificial intelligence > I460 - Machine learning
Divisions:	Faculty of Engineering and Physical Sciences > School of Electronic and Electrical Engineering
Related resources:	Location Type https://doi.org/10.1016/j.ultras.2024.107251 Publication
License:	Creative Commons Attribution 4.0 International (CC BY 4.0)
Date deposited:	22 Mar 2024 15:32
URI:	https://archive.researchdata.leeds.ac.uk/id/eprint/1263
Additional details Additional details Creators: Creators ORCID Other ID Email Primary affiliation Primary affiliation ID Primary affiliation ID type Secondary affiliation Secondary affiliation ID Secondary affiliation ID type McLaughlan, James https://orcid.org/0000-0001-5795-4372 University of Leeds https://ror.org/024mrxd33 ror Howell, Lewis https://orcid.org/0000-0003-4807-4087 University of Leeds https://ror.org/024mrxd33 ror Ingram, Nicola https://orcid.org/0000-0001-5274-8502 University of Leeds https://ror.org/024mrxd33 ror Type of data: Dataset Research funders: UKRI Grant numbers: EP/V043714/1 Publication date: 2 October 2024 Resource language: English Publisher: University of Leeds Contact Email Address: j.r.mclaughlan@leeds.ac.uk Last Modified: 22 Mar 2024 23:35

Files

Documentation

readme_McLaughlan_2024.txt [2kB] readme_McLaughlan_2024.txt [2kB] readme_McLaughlan_2024.txt [2kB] Visible to: Anyone File name: readme_McLaughlan_2024.txt Content type: Documentation Mime-Type: text/plain License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 2kB

Data

VideoClips [135MB] VideoClips [135MB] VideoClips [135MB] Visible to: Anyone File name: VideoClips.zip Content type: Data Description: All 2s ultrasound scans of COVID-19 phantom Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 135MB

LUS_model [91MB] LUS_model [91MB] LUS_model [91MB] Visible to: Anyone File name: LUS_model.zip Content type: Data Description: a directory with the training and test data used to train and validate the ML used in the accompanying paper Mime-Type: application/zip License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 91MB

Program

vidtoImage [2kB] vidtoImage [2kB] vidtoImage [2kB] Visible to: Anyone File name: vidtoImage.m Content type: Program Description: A MatLab program for extracting image frames from video data, and randomly assigning it folders for labelling Mime-Type: application/vnd.wolfram.mathematica.package License: Creative Commons Attribution 4.0 International (CC BY 4.0) File size: 2kB