ISSN: 1899-0967
Polish Journal of Radiology
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vol. 88
Chest radiology
Original paper

Influence of augmentation on the performance of double ResNet-based model for chest X-rays classification

Anna Kloska
Martyna Tarczewska
Agata Giełczyk
Sylwester Michał Kloska
Adrian Michalski

Faculty of Medicine, Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Torun, Poland
Bydgoszcz University of Science and Technology, Bydgoszcz, Poland
Department of Analytical Chemistry, Ludwik Rydygier Collegium Medicum, Nicolaus Copernicus University in Torun, Poland
© Pol J Radiol 2023; 88: e244-e250
Online publish date: 2023/05/12
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A pandemic disease elicited by the SARS-CoV-2 virus has become a serious health issue due to infecting millions of people all over the world. Recent publications prove that artificial intelligence (AI) can be used for medical diagnosis purposes, including interpretation of X-ray images. X-ray scanning is relatively cheap, and scan processing is not computationally demanding.

Material and methods:
In our experiment a baseline transfer learning schema of processing of lung X-ray images, including augmentation, in order to detect COVID-19 symptoms was implemented. Seven different scenarios of augmentation were proposed. The model was trained on a dataset consisting of more than 30,000 X-ray images.

The obtained model was evaluated using real images from a Polish hospital, with the use of standard metrics, and it achieved accuracy = 0.9839, precision = 0.9697, recall = 1.0000, and F1-score = 0.9846.

Our experiment proved that augmentations and masking could be important steps of data pre-processing and could contribute to improvement of the evaluation metrics. Because medical professionals often tend to lack confidence in AI-based tools, we have designed the proposed model so that its results would be explainable and could play a supporting role for radiology specialists in their work.


image processing, data augmentation, machine learning, COVID-19

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