Urine Sediment Recognition Method Based on Multi-View Deep Residual Learning in Microscopic Image.
Urine sediment recognition is attracting growing interest in the field of computer vision. A multi-view urine cell recognition method based on multi-view deep residual learning is proposed to solve some existing problems, such as multi-view cell gray change and cell information loss in the natural s...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 11; pp. 1 - 19 |
|---|---|
| Autores principales: | , , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2019
|
| 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=142115172&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142115172 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Nov2019 vid: 43 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142115172 10.1007/s10916-019-1457-4 142115172 ppf: 1 ppct: 18 formats: fmt: @attributes: type: P tig: atl: Urine Sediment Recognition Method Based on Multi-View Deep Residual Learning in Microscopic Image. aug: au: Zhang, Xiaohong Jiang, Liqing Yang, Dongxu Yan, Jinyan Lu, Xinhong affil: Affiliated Hospital of Jining Medical University, Jining No.1 people’s hospital, 272000, Jining, Shandong, China sug: ab: Urine sediment recognition is attracting growing interest in the field of computer vision. A multi-view urine cell recognition method based on multi-view deep residual learning is proposed to solve some existing problems, such as multi-view cell gray change and cell information loss in the natural state. Firstly, the convolutional network is designed to extract the urine sediment features from different perspectives based on the residual network, and the depth-wise separable convolution is introduced to reduce the network parameters. Secondly, Squeeze-and-Excitation block is embedded to learn feature weights, using feature re-calibration to improve network representation, and the robustness of the network is enhanced by adding spatial pyramid pooling. Finally, for further optimizing the recognition results, the Adam with weight decay optimization method is used to accelerate the convergence of the network model. Experiments on self-built urine microscopic image data-set show that our proposed method has state-of-the-art classification accuracy and reduces network computing time. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|