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DC Field | Value | Language |
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dc.contributor.author | Yan, F | - |
dc.contributor.author | Huang, X | - |
dc.contributor.author | Yao, Y | - |
dc.contributor.author | Lu, M | - |
dc.contributor.author | Li, M | - |
dc.date.accessioned | 2019-11-05T16:29:27Z | - |
dc.date.available | 2019-06-03 | - |
dc.date.available | 2019-11-05T16:29:27Z | - |
dc.date.issued | 2019-06-03 | - |
dc.identifier.citation | IEEE Access, 2019, 7 pp. 74181 - 74189 | en_US |
dc.identifier.issn | 2169-3536 | - |
dc.identifier.issn | http://dx.doi.org/10.1109/ACCESS.2019.2920397 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/19499 | - |
dc.description.abstract | The chest X-ray is a simple and economical medical aid for auxiliary diagnosis and therefore hasbecomearoutineitemforresidents’physicalexaminations.Basedon40167imagesofchestradiographs and corresponding reports, we explore the abnormality classification problem of chest X-rays by taking advantage of deep learning techniques. First of all, since the radiology reports are generally templatized by theaberrantphysicalregions,weproposeanannotationmethodaccordingtotheabnormalpartintheimages. Second, building on a small number of reports that are manually annotated by professional radiologists, we employ the long short-term memory (LSTM) model to automatically annotate the remaining unlabeled data.Theresultshowsthattheprecisionvaluereaches0.88inaccuratelyannotatingimages,therecallvalue reaches 0.85, and the F1-score reaches 0.86. Finally, we classify the abnormality in the chest X-rays by training convolutional neural networks, and the results show that the average AUC value reaches 0.835. | en_US |
dc.format.extent | 74181 - 74189 | - |
dc.language.iso | en | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers | en_US |
dc.subject | Annotation, | en_US |
dc.subject | deep neural network, | en_US |
dc.subject | DenseNet, | en_US |
dc.subject | long short term memory | en_US |
dc.title | Combining LSTM and DenseNet for Automatic Annotation and Classification of Chest X-Ray Images | en_US |
dc.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1109/ACCESS.2019.2920397 | - |
dc.relation.isPartOf | IEEE Access | - |
pubs.publication-status | Published | - |
pubs.volume | 7 | - |
Appears in Collections: | Publications Publications |
Files in This Item:
File | Description | Size | Format | |
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Fulltext.pdf | 6.58 MB | Adobe PDF | View/Open |
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