Title | ||
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Correcting data imbalance for semi-supervised COVID-19 detection using X-ray chest images |
Abstract | ||
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A key factor in the fight against viral diseases such as the coronavirus (COVID-19) is the identification of virus carriers as early and quickly as possible, in a cheap and efficient manner. The application of deep learning for image classification of chest X-ray images of COVID-19 patients could become a useful pre-diagnostic detection methodology. However, deep learning architectures require large labelled datasets. This is often a limitation when the subject of research is relatively new as in the case of the virus outbreak, where dealing with small labelled datasets is a challenge. Moreover, in such context, the datasets are also highly imbalanced, with few observations from positive cases of the new disease. In this work we evaluate the performance of the semi-supervised deep learning architecture known as MixMatch with a very limited number of labelled observations and highly imbalanced labelled datasets. We demonstrate the critical impact of data imbalance to the model's accuracy. Therefore, we propose a simple approach for correcting data imbalance, by re-weighting each observation in the loss function, giving a higher weight to the observations corresponding to the under-represented class. For unlabelled observations, we use the pseudo and augmented labels calculated by MixMatch to choose the appropriate weight. The proposed method improved classification accuracy by up to 18%, with respect to the non balanced MixMatch algorithm. We tested our proposed approach with several available datasets using 10, 15 and 20 labelled observations, for binary classification (COVID-19 positive and normal cases). For multi-class classification (COVID-19 positive, pneumonia and normal cases), we tested 30, 50, 70 and 90 labelled observations. Additionally, a new dataset is included among the tested datasets, composed of chest X-ray images of Costa Rican adult patients. (C) 2021 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
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2021 | 10.1016/j.asoc.2021.107692 | APPLIED SOFT COMPUTING |
Keywords | DocType | Volume |
Coronavirus, COVID-19, Computer aided diagnosis, Data imbalance, Semi-supervised learning | Journal | 111 |
ISSN | Citations | PageRank |
1568-4946 | 0 | 0.34 |
References | Authors | |
0 | 7 |
Name | Order | Citations | PageRank |
---|---|---|---|
Saul Calderon-Ramirez | 1 | 2 | 2.05 |
Shengxiang-Yang | 2 | 0 | 0.34 |
Armaghan Moemeni | 3 | 0 | 1.69 |
David Elizondo | 4 | 0 | 0.34 |
Simon Colreavy-Donnelly | 5 | 0 | 1.35 |
Luis Fernando Chavarria-Estrada | 6 | 0 | 0.34 |
Miguel A. Molina-Cabello | 7 | 1 | 6.44 |