ECM-CSD: An Efficient Classification Model for Cancer Stage Diagnosis in CT Lung Images Using FCM and SVM Techniques

  • M. S. KavithaEmail author
  • J. Shanthini
  • R. Sabitha
Image & Signal Processing
Part of the following topical collections:
  1. Wearable Computing Techniques for Smart Health


As is eminent, lung cancer is one of the death frightening syndromes among people in present cases. The earlier diagnosis and treatment of lung cancer can increase the endurance rate of the affected people. But, the structure of the cancer cell makes the diagnosis process more challenging, in which the most of the cells are superimposed. By adopting the efficient image processing techniques, the diagnosis process can be made effective, earlier and accurate, where the time aspect is extremely decisive. With those considerations, the main objective of this work is to propose a region based Fuzzy C-Means Clustering (FCM) technique for segmenting the lung cancer region and the Support Vector Machine (SVM) based classification for diagnosing the cancer stage, which helps in clinical practice in significant way to increase the morality rate. Moreover, the proposed ECM-CSD (Efficient Classification Model for Cancer Stage Diagnosis) uses Computed Tomography (CT) lung images for processing, since it poses higher imaging sensitivity, resolution with good isotopic acquisition in lung nodule identification. With those images, the pre-processing has been made with Gaussian Filter for smoothing and Gabor Filter for enhancement. Following, based on the extracted image features, the effective segmentation of lung nodules is performed using the FCM based clustering. And, the stages of cancer are identified based on the SVM classification technique. Further, the model is analyzed with MATLAB tool by incorporating the LIDC-IDRI lung CT images clinical dataset. The comparative experiments show the efficiency of the proposed model in terms of the performance evaluation factors like increased accuracy and reduced error rate.


CT lung image ECM-CSD (efficient classification model for cancer stage diagnosis) Fuzzy C-means clustering (FCM) Support vector machine (SVM) Segmentation 


Compliance with ethical standards

Conflict of interest

The authors have no conflict of interests and the paper has not beensubmitted elsewhere.

Research involving human participants and/or animals

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

Informed consent

The work does not involve any human or animal participants. The datasets used in the work are taken from free online sources.


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

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringSNS College of TechnologyCoimbatoreIndia

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