A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment.
During mammogram screening, there is a higher probability that detection of cancers is missed, and more than 16 percentage of breast cancer is not detected by radiologists. This problem can be solved by employing image processing algorithms which enhances the accuracy of the diagnostic through image...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 6; pp. 1 - 11 |
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| Autores principales: | , , , |
| Formato: | diagnostic images equations & formulas pictorial review statistics tables/charts Journal Article |
| Publicado: |
Springer Nature
Jun2019
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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=136503272&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 136503272 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Jun2019 vid: 43 iid: 6 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 136503272 136503272 136503272 10.1007/s10916-019-1302-9 136503272 ppf: 1 ppct: 10 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Novel Internet of Things Framework Integrated with Real Time Monitoring for Intelligent Healthcare Environment. aug: au: Suresh, A. Udendhran, R. Balamurgan, M. Varatharajan, R. affil: Department of Computer Science and Engineering, Nehru Institute of Engineering and Technology, T.M.Palayam, 641105, Coimbatore, TamilNadu, India sug: subj: Internet of Things Conceptual Framework Monitoring, Physiologic Artificial Intelligence Health Facility Environment Psychosocial Factors Algorithms Systems Integration Breast Neoplasms Diagnosis Mammography ROC Curve Image Processing, Computer Assisted Neural Networks (Computer) Breast Neoplasms Classification Biopsy, Needle Decision Trees ab: During mammogram screening, there is a higher probability that detection of cancers is missed, and more than 16 percentage of breast cancer is not detected by radiologists. This problem can be solved by employing image processing algorithms which enhances the accuracy of the diagnostic through image segmentation which reduces the misclassified malignant cancers. By employing segmentation, the unnecessary regions in the breast close to the boundary between the breast tissue and segmented pectoral muscle can be removed, therefore enhancing the accuracy the calculation as well as feature estimation. In-order to enhance the accuracy of classification, the proposed classifier integrates the decision trees and neural network into a system to report the progress of the breast cancer patients in an appropriate manner with the help of technology used in healthcare system. The proposed classifier successfully demonstrated that it achieved more accurate prediction when compared with other widely used algorithms, namely, K-Nearest Neighbors, Support Vector Machine and Naive Bayes algorithm. pubtype: Academic Journal doctype: diagnostic images equations & formulas pictorial review statistics tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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