Deep Stacked Patch Auto-Encoder Based Organ Classification For Surgical Data Science Applications.
A deep learning based robust classification is presented using Deep SPAE (Stacked Patch Auto-Encoder) optimized using Black-Widow Optimization (BWO) algorithm in anatomical structures which is commonly called as Deep SPAE_BWO. Initially, pre-processing is done using GIF (Guided Image Filter) elimina...
| Publicado en: | Turkish Journal of Physiotherapy Rehabilitation Vol. 32; no. 2; pp. 770 - 781 |
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| Autores principales: | , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Turkish Journal of Physiotherapy & Rehabilitation
2021
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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=151006032&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151006032 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 13008757 YU1 jtl: Turkish Journal of Physiotherapy Rehabilitation issn: 13008757 maglogo: N pubinfo: dt: 2021 vid: 32 iid: 2 pid: 20392 pub: Turkish Journal of Physiotherapy & Rehabilitation place: Kizilay/ Ankara, <Blank> artinfo: ui: 151006032 151006032 151006032 151006032 ppf: 770 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Deep Stacked Patch Auto-Encoder Based Organ Classification For Surgical Data Science Applications. aug: au: KONDURI, PRAVEEN S. R. RAO, G. SIVA NAGESWARA affil: PhD Scholar, Department of Computer Science and Engineering, KoneruLakshmaiah Education Foundation, Vaddeswaram, AP, India sug: subj: Deep Learning Data Science Predictive Value of Tests Physics Algorithms Sensitivity and Specificity ab: A deep learning based robust classification is presented using Deep SPAE (Stacked Patch Auto-Encoder) optimized using Black-Widow Optimization (BWO) algorithm in anatomical structures which is commonly called as Deep SPAE_BWO. Initially, pre-processing is done using GIF (Guided Image Filter) eliminates the noise and preserves the sharp edges. Next normalization using Min_Max method makes changes in the range of pixel values. Then, the image structures which possess similar characteristics are grouped with Modified C- Mean (MC-M) clustering. Feature Extraction is done using Entropy based Local Binary Pattern (ELBP), Grey Level Co-occurrence Matrix (GLCM) and Grey Level Run Length Matrix (GLRLM) to extract the optimal features. The tissues may be incompletely visible and may look different across images. So, Deep SPAE_BWObased classification is used to classify the anatomical structures followed by automatic confidence estimation named Gini-co-efficient (GC). Finally, an automatic image tagging (IT) approach is presented to label the classified images as Liver, Gallbladder and Fat Tissues. This work is implemented in PYTHON tool. The classification performance is evaluated in terms of precision, specificity, accuracy, sensitivity, NPV (Negative Predictive Value) and F1-score. pubtype: Academic Journal doctype: equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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