An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and Classification.
In this paper, we propose a new Internet of Things (IoT) based predictive modelling by using fuzzy cluster based augmentation and classification for predicting the lung cancer disease through continuous monitoring and also to improve the healthcare by providing medical instructions. Here, the fuzzy...
| Publicado en: | Journal of Medical Systems Vol. 43; no. 2; pp. 1 - 2 |
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| Autores principales: | , |
| Formato: | algorithm diagnostic images equations & formulas review tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2019
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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=134561906&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134561906 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Feb2019 vid: 43 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134561906 134561906 134561906 10.1007/s10916-018-1139-7 134561906 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and Classification. aug: au: Palani, D. Venkatalakshmi, K. affil: Department of Electronics and Communication Engineering, University College of Engineering Villupuram, 605103, Villupuram, Tamilnadu, India sug: subj: Internet of Things Lung Neoplasms Diagnosis Image Processing, Computer Assisted Image Interpretation, Computer Assisted Quality Improvement Neural Networks (Computer) Decision Trees Algorithms Classification Systems Design User-Computer Interface Machine Learning Models, Theoretical Data Mining ab: In this paper, we propose a new Internet of Things (IoT) based predictive modelling by using fuzzy cluster based augmentation and classification for predicting the lung cancer disease through continuous monitoring and also to improve the healthcare by providing medical instructions. Here, the fuzzy clustering method is used and which is based on transition region extraction for effective image segmentation. Moreover, Fuzzy C-Means Clustering algorithm is used to categorize the transitional region features from the feature of lung cancer image. In this work, Otsu thresholding method is used for extracting the transition region from lung cancer image. Moreover, the right edge image and the morphological thinning operation are used for enhancing the performance of segmentation. In addition, the morphological cleaning and the image region filling operations are performed over an edge lung cancer image for getting the object regions. In addition, we also propose a new incremental classification algorithm which combines the existing Association Rule Mining (ARM), the standard Decision Tree (DT) with temporal features and the CNN. The experiments have been conducted by using the standard images that are collected from database and the current health data which are collected from patient through IoT devices. The results proved that the performance of the proposed prediction model which is able to achieve the better accuracy when it is compared with other existing prediction model. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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