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title

Detection of Lung Cancer Using CT Images Based on Novel PSO Clustering

Credit to Download: 1 | Page Numbers 10 | Abstract Views: 120
Year: 2017
COI code: IIEC14_061
Paper Language: English

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Authors Detection of Lung Cancer Using CT Images Based on Novel PSO Clustering

  Fatemeh Sadeghi - Department of Industrial Engineering and Management Systems, Amirkabir University of Technology,Thehran,Iran
  Abbas Ahmadi - Department of Industrial Engineering and Management Systems, Amirkabir University of Technology,Thehran,Iran

Abstract:

Lung cancer is one of the most dangerous diseases that cause a large number of deaths. Early detection and analysis can be very helpful for successful treatment. Image segmentation plays a key role in the early detection and diagnosis of lung cancer. K-means algorithm and classic PSO clustering are the most common methods for segmentation that have poor outputs. In this article, we propose a new modified PSO method. The performance of the proposed algorithm is compared to that of K-means and classic PSO clustering. The obtained results show that the new PSO clustering has better results as compared to the other methods. Comparison between the proposed method and classic PSO, in terms of fitness function and convergence of fitness function indicate that the proposed method is more effective in detecting lung cancer

Keywords:

lung cancer, imge clustering, PSO clustering

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COI code: IIEC14_061

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Sadeghi, Fatemeh & Abbas Ahmadi, 2017, Detection of Lung Cancer Using CT Images Based on Novel PSO Clustering, 14th International Industrial Engineering Conference, تهران, انجمن مهندسي صنايع ايران - دانشگاه علم و صنعت ايران, https://www.civilica.com/Paper-IIEC14-IIEC14_061.htmlInside the text, wherever referred to or an achievement of this article is mentioned, after mentioning the article, inside the parental, the following specifications are written.
First Time: (Sadeghi, Fatemeh & Abbas Ahmadi, 2017)
Second and more: (Sadeghi & Ahmadi, 2017)
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Type: state university
Paper No.: 19662
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