2021
DOI: 10.1016/j.asoc.2021.107692
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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 t… Show more

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Cited by 33 publications
(37 citation statements)
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“…Previous studies already used the BIMCV-COVID dataset to evaluate lung segmentation (32), data imbalance corrections (33), DL classification models (34)(35)(36), and other imaging challenges (37,38).…”
Section: Image Datasetmentioning
confidence: 99%
“…Previous studies already used the BIMCV-COVID dataset to evaluate lung segmentation (32), data imbalance corrections (33), DL classification models (34)(35)(36), and other imaging challenges (37,38).…”
Section: Image Datasetmentioning
confidence: 99%
“…Another approach to deal with small labelled datasets is the usage of SSDL, which leverages unlabelled data to improve the model's performance [20]. In recent years, the usage of the cheaper and larger unlabelled datasets for training deep learning models has proven to be a viable option for handling the lack of labelled data, as well as improving the performance of models [13,17]. Authors in [20] present a survey of recent literature of semi-supervised learning approaches for medical imaging.…”
Section: Semi-supervised Learning For Medical Imaging and Mammogram A...mentioning
confidence: 99%
“…The survey shows how unlabelled datasets have been used for improving model training in brain tumour segmentation, detection of vascular lesions, and prostate cancer detection. More recently, the usage of unlabelled data with semi-supervised deep learning has proven to give positive results in the detection of COVID-19 in chest x-ray images [13,17].…”
Section: Semi-supervised Learning For Medical Imaging and Mammogram A...mentioning
confidence: 99%
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