Lung Nodule Detection based on Ensemble of Hand Crafted and Deep Features.
Lung cancer is considered as a deadliest disease worldwide due to which 1.76 million deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer detection at a premature stage is a more significant requirement to reduce the probability of mortality rate. This manuscript d...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 12; pp. 1 - 13 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas 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=140292671&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 140292671 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: 140292671 140292671 140292671 10.1007/s10916-019-1455-6 140292671 ppf: 1 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Lung Nodule Detection based on Ensemble of Hand Crafted and Deep Features. aug: au: Saba, Tanzila Sameh, Ahmed Khan, Fatima Shad, Shafqat Ali Sharif, Muhammad affil: College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia sug: subj: Lung Neoplasms Radiography Image Processing, Computer Assisted Methods Algorithms Lung Neoplasms Surgery Lung Neoplasms Classification Deep Learning Motivation Image Enhancement Manuscripts ab: Lung cancer is considered as a deadliest disease worldwide due to which 1.76 million deaths occurred in the year 2018. Keeping in view its dreadful effect on humans, cancer detection at a premature stage is a more significant requirement to reduce the probability of mortality rate. This manuscript depicts an approach of finding lung nodule at an initial stage that comprises of three major phases: (1) lung nodule segmentation using Otsu threshold followed by morphological operation; (2) extraction of geometrical, texture and deep learning features for selecting optimal features; (3) The optimal features are fused serially for classification of lung nodule into two categories that is malignant and benign. The lung image database consortium image database resource initiative (LIDC-IDRI) is used for experimentation. The experimental outcomes show better performance of presented approach as compared with the existing methods. pubtype: Academic Journal doctype: diagnostic images equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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