A multi-label classification model for full slice brain computerised tomography image

Simple item page
dc.citation.issueSuppl 6
dc.citation.volume21
dc.contributor.authorLi J
dc.contributor.authorFu G
dc.contributor.authorChen Y
dc.contributor.authorLi P
dc.contributor.authorLiu B
dc.contributor.authorPei Y
dc.contributor.authorFeng H
dc.coverage.spatialEngland
dc.date.accessioned2023-11-15T23:47:47Z
dc.date.accessioned2023-11-20T01:38:05Z
dc.date.available2020-11-18
dc.date.available2023-11-15T23:47:47Z
dc.date.available2023-11-20T01:38:05Z
dc.date.issued2020-11-18
dc.description.abstractBACKGROUND: Screening of the brain computerised tomography (CT) images is a primary method currently used for initial detection of patients with brain trauma or other conditions. In recent years, deep learning technique has shown remarkable advantages in the clinical practice. Researchers have attempted to use deep learning methods to detect brain diseases from CT images. Methods often used to detect diseases choose images with visible lesions from full-slice brain CT scans, which need to be labelled by doctors. This is an inaccurate method because doctors detect brain disease from a full sequence scan of CT images and one patient may have multiple concurrent conditions in practice. The method cannot take into account the dependencies between the slices and the causal relationships among various brain diseases. Moreover, labelling images slice by slice spends much time and expense. Detecting multiple diseases from full slice brain CT images is, therefore, an important research subject with practical implications. RESULTS: In this paper, we propose a model called the slice dependencies learning model (SDLM). It learns image features from a series of variable length brain CT images and slice dependencies between different slices in a set of images to predict abnormalities. The model is necessary to only label the disease reflected in the full-slice brain scan. We use the CQ500 dataset to evaluate our proposed model, which contains 1194 full sets of CT scans from a total of 491 subjects. Each set of data from one subject contains scans with one to eight different slice thicknesses and various diseases that are captured in a range of 30 to 396 slices in a set. The evaluation results present that the precision is 67.57%, the recall is 61.04%, the F1 score is 0.6412, and the areas under the receiver operating characteristic curves (AUCs) is 0.8934. CONCLUSION: The proposed model is a new architecture that uses a full-slice brain CT scan for multi-label classification, unlike the traditional methods which only classify the brain images at the slice level. It has great potential for application to multi-label detection problems, especially with regard to the brain CT images.
dc.description.confidentialfalse
dc.format.pagination200-
dc.identifier.author-urlhttps://www.ncbi.nlm.nih.gov/pubmed/33203366
dc.identifier.citationLi J, Fu G, Chen Y, Li P, Liu B, Pei Y, Feng H. (2020). A multi-label classification model for full slice brain computerised tomography image.. BMC Bioinformatics. 21. Suppl 6. (pp. 200-).
dc.identifier.doi10.1186/s12859-020-3503-0
dc.identifier.eissn1471-2105
dc.identifier.elements-typejournal-article
dc.identifier.issn1471-2105
dc.identifier.number200
dc.identifier.pii10.1186/s12859-020-3503-0
dc.identifier.urihttps://mro.massey.ac.nz/handle/10179/69154
dc.languageeng
dc.publisherBioMed Central Ltd
dc.publisher.urihttps://bmcbioinformatics.biomedcentral.com/articles/10.1186/s12859-020-3503-0
dc.relation.isPartOfBMC Bioinformatics
dc.rights(c) 2020 The Author/s
dc.rightsCC BY 4.0
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectBioinformatics
dc.subjectBrain computerised tomography
dc.subjectComputer aided diagnosis
dc.subjectDeep learning
dc.subjectMachine learning
dc.subjectAlgorithms
dc.subjectBrain
dc.subjectHumans
dc.subjectTomography, X-Ray Computed
dc.titleA multi-label classification model for full slice brain computerised tomography image
dc.typeJournal article
pubs.elements-id446276
pubs.organisational-groupOther
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Published version
Size:
3.23 MB
Format:
Adobe Portable Document Format
Description:
Download
Collections
Journal Articles