Journal Article

FZJ-2023-00913

http://join2-wiki.gsi.de/foswiki/pub/Main/Artwork/join2_logo100x88.png 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

Barakat, C. (Corresponding author)FZJ* ; Aach, M.FZJ* ; Schuppert, A.RWTH* ; Brynjólfsson, S. ; Fritsch, S.FZJ* ; Riedel, M.FZJ*

2023
MDPI Basel

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Please use a persistent id in citations: http://hdl.handle.net/2128/33768  doi:10.3390/diagnostics13030391

Abstract: 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.

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Contributing Institute(s):

1. Jülich Supercomputing Center (JSC)

Research Program(s):

1. 5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and Research Groups (POF4-511) (POF4-511)
2. RAISE - Research on AI- and Simulation-Based Engineering at Exascale (951733) (951733)
3. EUROCC - National Competence Centres in the framework of EuroHPC (951732) (951732)
4. SMITH - Medizininformatik-Konsortium - Beitrag Forschungszentrum Jülich (01ZZ1803M) (01ZZ1803M)

Appears in the scientific report 2023

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Database coverage:
; ; ; ; Article Processing Charges ; Clarivate Analytics Master Journal List ; Current Contents - Clinical Medicine ; DOAJ Seal ; Ebsco Academic Search ; Essential Science Indicators ; Fees ; IF < 5 ; JCR ; PubMed Central ; SCOPUS ; Science Citation Index Expanded ; Web of Science Core Collection

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