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Journal of Medical Systems

, 43:21 | Cite as

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

  • D. PalaniEmail author
  • K. Venkatalakshmi
Image & Signal Processing
  • 121 Downloads
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health

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.

Keywords

Transitional image extraction Edge detection Segmentation Fuzzy C-means clustering Lung cancer image Association rule mining (ARM) Decision tree (DT) Incremental classification Convolutional neural network (CNN) Internet of things (IoT) 

Notes

Compliance with Ethical Standards

Conflict of Interest

The authors have no conflict of interest.

(In Case Animals Were Involved) Ethical Approval

Animals were not involved.

(And/or in Case Humans Were Involved) Ethical Approval

This article does not contain any studies with human participants performed by any of the authors.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electronics and Communication EngineeringUniversity College of Engineering VillupuramVillupuramIndia
  2. 2.Department of Electronics and Communication EngineeringUniversity College of Engineering TindivanamTindivanamIndia

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