Stomach Deformities Recognition Using Rank-Based Deep Features Selection.
Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complet...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 12; pp. 1 - 16 |
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| Autores principales: | , , , , |
| Formato: | equations & formulas pictorial tables/charts Journal Article |
| Publicado: |
Springer Nature
Dec2019
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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=140292673&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140292673 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Dec2019 vid: 43 iid: 12 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 140292673 140292673 140292673 10.1007/s10916-019-1466-3 140292673 ppf: 1 ppct: 15 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Stomach Deformities Recognition Using Rank-Based Deep Features Selection. aug: au: Khan, Muhammad Attique Sharif, Muhammad Akram, Tallha Yasmin, Mussarat Nayak, Ramesh Sunder affil: Department of CS&E, HITEC University, Museum Road, Taxila, Pakistan sug: subj: Gastrointestinal Diseases Diagnosis Intraabdominal Infections Diagnosis Intraabdominal Infections Classification Capsule Endoscopy Methods Image Processing, Computer Assisted Colorectal Neoplasms Diagnosis Automation Peptic Ulcer Gastrointestinal Hemorrhage ab: Doctor utilizes various kinds of clinical technologies like MRI, endoscopy, CT scan, etc., to identify patient's deformity during the review time. Among set of clinical technologies, wireless capsule endoscopy (WCE) is an advanced procedures used for digestive track malformation. During this complete process, more than 57,000 frames are captured and doctors need to examine a complete video frame by frame which is a tedious task even for an experienced gastrologist. In this article, a novel computerized automated method is proposed for the classification of abdominal infections of gastrointestinal track from WCE images. Three core steps of the suggested system belong to the category of segmentation, deep features extraction and fusion followed by robust features selection. The ulcer abnormalities from WCE videos are initially extracted through a proposed color features based low level and high-level saliency (CFbLHS) estimation method. Later, DenseNet CNN model is utilized and through transfer learning (TL) features are computed prior to feature optimization using Kapur's entropy. A parallel fusion methodology is opted for the selection of maximum feature value (PMFV). For feature selection, Tsallis entropy is calculated later sorted into descending order. Finally, top 50% high ranked features are selected for classification using multilayered feedforward neural network classifier for recognition. Simulation is performed on collected WCE dataset and achieved maximum accuracy of 99.5% in 21.15 s. pubtype: Academic Journal doctype: equations & formulas pictorial tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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