TY  - JOUR
AU  - Barakat, Chadi
AU  - Aach, Marcel
AU  - Schuppert, Andreas
AU  - Brynjólfsson, Sigurður
AU  - Fritsch, Sebastian
AU  - Riedel, Morris
TI  - Analysis of Chest X-ray for COVID-19 Diagnosis as a Use Case for an HPC-Enabled Data Analysis and Machine Learning Platform for Medical Diagnosis Support
JO  - Diagnostics
VL  - 13
IS  - 3
SN  - 2075-4418
CY  - Basel
PB  - MDPI
M1  - FZJ-2023-00913
SP  - 391
PY  - 2023
AB  - The COVID-19 pandemic shed light on the need for quick diagnosis tools in healthcare, leading to the development of several algorithmic models for disease detection. Though these models are relatively easy to build, their training requires a lot of data, storage, and resources, which may not be available for use by medical institutions or could be beyond the skillset of the people who most need these tools. This paper describes a data analysis and machine learning platform that takes advantage of high-performance computing infrastructure for medical diagnosis support applications. This platform is validated by re-training a previously published deep learning model (COVID-Net) on new data, where it is shown that the performance of the model is improved through large-scale hyperparameter optimisation that uncovered optimal training parameter combinations. The per-class accuracy of the model, especially for COVID-19 and pneumonia, is higher when using the tuned hyperparameters (healthy: 96.5%; pneumonia: 61.5%; COVID-19: 78.9%) as opposed to parameters chosen through traditional methods (healthy: 93.6%; pneumonia: 46.1%; COVID-19: 76.3%). Furthermore, training speed-up analysis shows a major decrease in training time as resources increase, from 207 min using 1 node to 54 min when distributed over 32 nodes, but highlights the presence of a cut-off point where the communication overhead begins to affect performance. The developed platform is intended to provide the medical field with a technical environment for developing novel portable artificial-intelligence-based tools for diagnosis support.
LB  - PUB:(DE-HGF)16
C6  - 36766496
UR  - <Go to ISI:>//WOS:000933780900001
DO  - DOI:10.3390/diagnostics13030391
UR  - https://juser.fz-juelich.de/record/943307
ER  -