Fog Computing Employed Computer Aided Cancer Classification System Using Deep Neural Network in Internet of Things Based Healthcare System.
Computer assisted automatic smart pattern analysis of cancer affected pixel structure takes critical role in pre-interventional decision making for oral cancer treatment. Internet of Things (IoT) in healthcare systems is now emerging solution for modern e-healthcare system to provide high quality me...
| Publicado en: | Journal of Medical Systems Vol. 44; no. 2; pp. 1 - 11 |
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| Autores principales: | , , , |
| Formato: | computer program diagnostic images equations & formulas 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=141512192&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 141512192 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Feb2020 vid: 44 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 141512192 141512192 141512192 10.1007/s10916-019-1500-5 141512192 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Fog Computing Employed Computer Aided Cancer Classification System Using Deep Neural Network in Internet of Things Based Healthcare System. aug: au: Rajan, J. Pandia Rajan, S. Edward Martis, Roshan Joy Panigrahi, B. K. affil: Mepco Schlenk Engineering College, Sivakasi, Tamil Nadu, India sug: subj: Cloud Computing Mouth Neoplasms Diagnosis Deep Learning Neural Networks (Computer) Internet of Things Health Care Delivery Methods Human Algorithms Diagnostic Imaging Data Analysis, Statistical Methods Tomography, Emission-Computed Neoplasms Sensitivity and Specificity ab: Computer assisted automatic smart pattern analysis of cancer affected pixel structure takes critical role in pre-interventional decision making for oral cancer treatment. Internet of Things (IoT) in healthcare systems is now emerging solution for modern e-healthcare system to provide high quality medical care. In this research work, we proposed a novel method which utilizes a modified vesselness measurement and a Deep Convolutional Neural Network (DCNN) to identify the oral cancer region structure in IoT based smart healthcare system. The robust vesselness filtering scheme handles noise while reserving small structures, while the CNN framework considerably improves classification accuracy by deblurring focused region of interest (ROI) through integrating with multi-dimensional information from feature vector selection step. The marked feature vector points are extracted from each connected component in the region and used as input for training the CNN. During classification, each connected part is individually analysed using the trained DCNN by considering the feature vector values that belong to its region. For a training of 1500 image dataset, an accuracy of 96.8% and sensitivity of 92% is obtained. Hence, the results of this work validate that the proposed algorithm is effective and accurate in terms of classification of oral cancer region in accurate decision making. The developed system can be used in IoT based diagnosis in health care systems, where accuracy and real time diagnosis are essential. pubtype: Academic Journal doctype: computer program diagnostic images equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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