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An enhanced computer-assisted lung cancer detection method using content based image retrieval and data mining techniques

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Abstract

Cancer is a sickness brought about by an uncontrolled division of eccentric cells in any part of human body. It is in the top of few places in the killer disease list and pervades in the entire world, but still on the rise. Most of the cases an early detection of lung cancer is cumbersome. This research paper is aimed to present an effective and an efficient way of computer-assisted detection method for lung cancer. In this research we used a set of lung computed tomography scanned images as inputs, obtained from lung image archives and applied image processing techniques such as feature extraction, segmentation. In this approach, a proper combination of Adaptive thresholding segmentation algorithm has been used for segmenting input images, a well-known Support Vector Machine image classification algorithm has been used for lung tumor classification and Content-based image retrieval technique has been used to compare lung image features such as contract, intensity, texture and shape. A set of patient personal data is included to get more accurate and correct prediction results, and it is dealt with data mining approach. The proposed segmentation method shows improved prediction results.

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Authors and Affiliations

  1. Department of CSE, Kings Engineering College, Irungattukottai, Chennai, TN, India

    B. Muthazhagan

  2. Department of CSE, Shadan Womens College of Engineering & Technology, Khairatabad, Hyderabad, Telangana, India

    T. Ravi

  3. Department of CSE, Sri Muthukumaran Institute of Technology, Chennai, TN, India

    D. Rajinigirinath

Authors
  1. B. Muthazhagan
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  2. T. Ravi
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  3. D. Rajinigirinath
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Correspondence to B. Muthazhagan.

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Muthazhagan, B., Ravi, T. & Rajinigirinath, D. An enhanced computer-assisted lung cancer detection method using content based image retrieval and data mining techniques. J Ambient Intell Human Comput (2020). https://doi.org/10.1007/s12652-020-02123-7

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  • DOI: https://doi.org/10.1007/s12652-020-02123-7

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