Classification of pressure ulcer tissues with 3D convolutional neural network.
A 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region of interest (ROI), the features are extracted from...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 56; no. 12; pp. 2245 - 2259 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Dec2018
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=133056255&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 133056255 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Dec2018 vid: 56 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 133056255 133056255 NLM29949023 10.1007/s11517-018-1835-y NLM29949023 133056255 ppf: 2245 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of pressure ulcer tissues with 3D convolutional neural network. aug: au: García-Zapirain, Begoña Elmogy, Mohammed El-Baz, Ayman Elmaghraby, Adel S. affil: Facultad Ingeniería, Universidad de Deusto, Avda/Universidades 24, 48007, Bilbao, Spain sug: subj: Neural Networks (Computer) Pressure Ulcer Imaging, Three-Dimensional Methods Color Algorithms Image Processing, Computer Assisted Methods ab: A 3D convolution neural network (CNN) of deep learning architecture is supplied with essential visual features to accurately classify and segment granulation, necrotic eschar, and slough tissues in pressure ulcer color images. After finding a region of interest (ROI), the features are extracted from both the original and convolved with a pre-selected Gaussian kernel 3D HSI images, combined with first-order models of current and prior visual appearance. The models approximate empirical marginal probability distributions of voxel-wise signals with linear combinations of discrete Gaussians (LCDG). The framework was trained and tested on 193 color pressure ulcer images. The classification accuracy and robustness were evaluated using the Dice similarity coefficient (DSC), the percentage area distance (PAD), and the area under the ROC curve (AUC). The obtained preliminary DSC of 92%, PAD of 13%, and AUC of 95% are promising. Graphical Abstract The Classification of Pressure Ulcer Tissues Based on 3D Convolutional Neural Network. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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