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  5. Crafting Deep Learning Models for Classifying ECG Paper Printouts

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Article
en
2024

Crafting Deep Learning Models for Classifying ECG Paper Printouts

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0 Files

en
2024
Vol 51
Vol. 51
DOI: 10.22489/cinc.2024.210

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Professor Gregory Lip
Professor Gregory Lip

University of Liverpool

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Damian Kucharski
Arkadiusz Paweł Czerwiński
Agata M. Wijata
+4 more

Abstract

As part of the George B. Moody PhysioNet Challenge 2024, we (the GIRAFFE team) built an approach based on InceptionV3 to classify the electrocardiogram (ECG) images.To deal with the class imbalance, we use the Generalized Extreme Value activation function and loss weighting.For the classification task, our best model received a macro F -measure of 0.652 over the hidden test data.Because we had not submitted any unofficial phase entry, we were not included in the official rankings.

How to cite this publication

Damian Kucharski, Arkadiusz Paweł Czerwiński, Agata M. Wijata, Jacek Kawa, Yalin Zheng, Professor Gregory Lip, Jakub Nalepa (2024). Crafting Deep Learning Models for Classifying ECG Paper Printouts. , 51, DOI: https://doi.org/10.22489/cinc.2024.210.

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Publication Details

Type

Article

Year

2024

Authors

7

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0

Total Files

0

Language

en

DOI

https://doi.org/10.22489/cinc.2024.210

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