Diagnostic and Gradation Model of Osteoporosis Based on Improved Deep U-Net Network.
The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease diagnosis. However, because of X-ray image noise and the large difference between the bone shapes of patients under the condition of low contrast, exis...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 1; pp. 1 - 8 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jan2020
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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=141026249&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141026249 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jan2020 vid: 44 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141026249 141026249 141026249 10.1007/s10916-019-1502-3 141026249 ppf: 1 ppct: 7 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Diagnostic and Gradation Model of Osteoporosis Based on Improved Deep U-Net Network. aug: au: Liu, Jian Wang, Jian Ruan, Weiwei Lin, Chengshan Chen, Daguo affil: Department of Orthopedics, Tongde Hospital of Zhejiang Province, 310012, Zhejiang, Hangzhou, China sug: subj: Osteoporosis Diagnosis Algorithms Neural Networks (Computer) Bone Density Evaluation Human Bone and Bones Radiography Absorptiometry, Photon ROC Curve Descriptive Statistics Sensitivity and Specificity Minimum Data Set ab: The measurement of bone mineral density for osteoporosis has always been the focus of researchers because it plays an important role in bone disease diagnosis. However, because of X-ray image noise and the large difference between the bone shapes of patients under the condition of low contrast, existing osteoporosis diagnosis algorithms are difficult to obtain satisfactory results. This paper presents an improved osteoporosis diagnosis algorithm based on U-NET network. Firstly, the bone in the original image are marked and used to construct the data set. And then, by normalizing the input of each layer, it can be ensured that the input data distribution of each layer is stable, so that the purpose of accelerated training can be achieved. Finally, the energy function is calculated by combining the value of the softmax prediction class for each pixel on the final feature map with the Cross entropy loss function and all the segmented images are extracted to obtain the diagnostic result. As the experimental results show that the improved U-net can accurately solve the influence of image interference in the process of bone mineral density measurement. The recognition rate of U-net automatic diagnosis method is above 81%, and the diagnosis effect is better than other comparison methods. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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