article

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

Authors:

R. SabithaAuthors Info & Claims

Journal of Medical Systems, Volume 43, Issue 3

Pages 1 - 9

https://doi.org/10.1007/s10916-019-1190-z

Published: 01 March 2019 Publication History

Abstract

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.

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Cited By

Messabih HKerrache CCheriguene YAmadeo MAhmad F(2024)Machine learning solutions for mobile internet of things securityTransactions on Emerging Telecommunications Technologies10.1002/ett.504135:10Online publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1002/ett.5041
Xu Y(2021)Recognition and Volume Measurement of Intracranial Hematoma through CT Images under Intelligent Recognition AlgorithmScientific Programming10.1155/2021/80457192021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8045719
Shan RRezaei T(2021)Lung Cancer Diagnosis Based on an ANN Optimized by Improved TEO AlgorithmComputational Intelligence and Neuroscience10.1155/2021/60785242021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6078524
Show More Cited By

ECM-CSD: An Efficient Classification Model for Cancer Stage Diagnosis in CT Lung Images Using FCM and SVM Techniques
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning

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Published In

cover image Journal of Medical Systems

Journal of Medical Systems Volume 43, Issue 3

March 2019

356 pages

ISSN:0148-5598

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

Publisher

Plenum Press

United States

Publication History

Published: 01 March 2019

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Cited By

Messabih HKerrache CCheriguene YAmadeo MAhmad F(2024)Machine learning solutions for mobile internet of things securityTransactions on Emerging Telecommunications Technologies10.1002/ett.504135:10Online publication date: 9-Oct-2024
https://dl.acm.org/doi/10.1002/ett.5041
Xu Y(2021)Recognition and Volume Measurement of Intracranial Hematoma through CT Images under Intelligent Recognition AlgorithmScientific Programming10.1155/2021/80457192021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/8045719
Shan RRezaei T(2021)Lung Cancer Diagnosis Based on an ANN Optimized by Improved TEO AlgorithmComputational Intelligence and Neuroscience10.1155/2021/60785242021Online publication date: 1-Jan-2021
https://dl.acm.org/doi/10.1155/2021/6078524
Sabitha RAruna AKarthik SShanthini J(2021)Enhanced model for fake image detection (EMFID) using convolutional neural networks with histogram and wavelet based feature extractionsPattern Recognition Letters10.1016/j.patrec.2021.10.007152:C(195-201)Online publication date: 1-Dec-2021
https://dl.acm.org/doi/10.1016/j.patrec.2021.10.007
Gokulalakshmi AKarthik SKarthikeyan NKavitha M(2020)RETRACTED ARTICLE: ICM-BTD: improved classification model for brain tumor diagnosis using discrete wavelet transform-based feature extraction and SVM classifierSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05096-z24:24(18599-18609)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s00500-020-05096-z
Kavitha MShanthini JKarthikeyan N(2020)RETRACTED ARTICLE: Volumetric analysis framework for accurate segmentation and classification (VAF-ASC) of lung tumor from CT imagesSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-020-05081-624:24(18489-18497)Online publication date: 1-Dec-2020
https://dl.acm.org/doi/10.1007/s00500-020-05081-6
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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