Abstract：
: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.
journal_name
Appl Soft Computjournal_title
Applied soft computingauthors
Calderon-Ramirez S,Yang S,Moemeni A,Elizondo D,Colreavy-Donnelly S,Chavarría-Estrada LF,Molina-Cabello MAdoi
10.1016/j.asoc.2021.107692keywords:
["COVID-19","Computer aided diagnosis","Coronavirus","Data imbalance","Semi-supervised learning"]subject
Has Abstractpub_date
2021-11-01 00:00:00pages
107692eissn
1568-4946issn
1872-9681pii
S1568-4946(21)00613-Xjournal_volume
111pub_type
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