Classification of CT Scan Images of Lungs Using Deep Convolutional Neural Network with External Shape-Based Features.
In this paper, a simplified yet efficient architecture of a deep convolutional neural network is presented for lung image classification. The images used for classification are computed tomography (CT) scan images obtained from two scientifically used databases available publicly. Six external shape...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 1; pp. 252 - 262 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2020
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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=142164515&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142164515 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2020 vid: 33 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142164515 142164515 142164515 10.1007/s10278-019-00245-9 142164515 ppf: 252 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Classification of CT Scan Images of Lungs Using Deep Convolutional Neural Network with External Shape-Based Features. aug: au: Srivastava, Varun Purwar, Ravindra Kr. affil: University School of Information and Communication Technology, Guru Gobind Singh Indraprastha University, Dwarka Sector 16C, 110075, New Delhi, India sug: subj: Lung Radiography Tomography, X-Ray Computed Classification Deep Learning Neural Networks (Computer) Lung Human Image Processing, Computer Assisted Image Interpretation, Computer Assisted Resource Databases Evaluation Comparative Studies Biomedical Engineering Abstracting and Indexing Image Retrieval Methods Descriptive Statistics Models, Statistical ab: In this paper, a simplified yet efficient architecture of a deep convolutional neural network is presented for lung image classification. The images used for classification are computed tomography (CT) scan images obtained from two scientifically used databases available publicly. Six external shape-based features, viz. solidity, circularity, discrete Fourier transform of radial length (RL) function, histogram of oriented gradient (HOG), moment, and histogram of active contour image, have also been identified and embedded into the proposed convolutional neural network. The performance is measured in terms of average recall and average precision values and compared with six similar methods for biomedical image classification. The average precision obtained for the proposed system is found to be 95.26% and the average recall value is found to be 69.56% in average for the two databases. 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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