article

An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and Classification

Authors:

K. VenkatalakshmiAuthors Info & Claims

Journal of Medical Systems, Volume 43, Issue 2

Pages 1 - 12

https://doi.org/10.1007/s10916-018-1139-7

Published: 01 February 2019 Publication History

Abstract

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.

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Zhang JShen YZheng ZSun L(2022)A Robust Image Segmentation Framework Based on Nonlocal Total Variation Spectral TransformWireless Communications & Mobile Computing10.1155/2022/14427452022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1442745
Sworna NIslam AShatabda SIslam S(2021)Towards development of IoT-ML driven healthcare systemsJournal of Network and Computer Applications10.1016/j.jnca.2021.103244196:COnline publication date: 15-Dec-2021
https://dl.acm.org/doi/10.1016/j.jnca.2021.103244
Akhbarifar SJavadi HRahmani AHosseinzadeh M(2020)RETRACTED ARTICLE: A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environmentPersonal and Ubiquitous Computing10.1007/s00779-020-01475-327:3(697-713)Online publication date: 16-Nov-2020
https://dl.acm.org/doi/10.1007/s00779-020-01475-3
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Information & Contributors

Information

Published In

cover image Journal of Medical Systems

Journal of Medical Systems Volume 43, Issue 2

February 2019

247 pages

ISSN:0148-5598

Issue’s Table of Contents

Copyright © Copyright © 2019 Springer Science+Business Media, LLC, part of Springer Nature.

Publisher

Plenum Press

United States

Publication History

Published: 01 February 2019

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Cited By

Zhang JShen YZheng ZSun L(2022)A Robust Image Segmentation Framework Based on Nonlocal Total Variation Spectral TransformWireless Communications & Mobile Computing10.1155/2022/14427452022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1442745
Sworna NIslam AShatabda SIslam S(2021)Towards development of IoT-ML driven healthcare systemsJournal of Network and Computer Applications10.1016/j.jnca.2021.103244196:COnline publication date: 15-Dec-2021
https://dl.acm.org/doi/10.1016/j.jnca.2021.103244
Akhbarifar SJavadi HRahmani AHosseinzadeh M(2020)RETRACTED ARTICLE: A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environmentPersonal and Ubiquitous Computing10.1007/s00779-020-01475-327:3(697-713)Online publication date: 16-Nov-2020
https://dl.acm.org/doi/10.1007/s00779-020-01475-3
Revathi MJeya IDeepa S(2020)RETRACTED ARTICLE: Deep learning-based soft computing model for image classification applicationSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05048-724:24(18411-18430)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s00500-020-05048-7

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