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Efficient tumor volume measurement and segmentation approach for CT image based on twin support vector machines

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Abstract

Suspicious volumetric tumor (SVT) segmentation of a CT-image (CT\(_{i}\)) and analysing changes in the volume of tumor is a significantly challenging task for the identification of lung cancer. In this regard, we design a two-step suspicious volumetric tumor segmentation (SVTS) approach based on an adaptive multiple resolution contour (AMRC) models for effective SVT segmentation. First, the high-intensity-pixels edge centroid of SVT (HECS) method is designed to identify the SVT location in CT\(_{i}\), and these outcomes are subsequently conceding threshold values to fix the level set method (LSM). Second, HECS outcomes are recognised using particle swarm optimisation (PSO) which is harmonised twin support vector machines (TSVM) to achieve segmentation accuracy. An open-source tumor cancer imaging archive (TCIA) dataset, 529 abnormal tissues (ATs) of the lung from the lung image database consortium (LIDC), are conceded to assess the performance of the SVT segmentation approach. The average segmentation accuracy of NLTC, TCIA, and LIDC datasets are 73.19%, 76.21% and 75.89%, respectively, compared with standard benchmark approaches. Subsequently, our framework efficiently classified the normal and abnormal CT\(_{i}\) based on the SVT segmentation accuracy rate.

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Acknowledgements

This work was supported in part of Basic Science Research Programs of the Ministry of Education (NRF-2018R1A2B6005105) and in part by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2019R1A5A8080290).

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Tirumala Engineering College, Narsaraopet, AP, India

    K. Sathish & Y. V. Narayana

  2. Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, 38544, Republic of Korea

    M. S. Mekala

  3. RLRC Lab for Autonomous Vehicle Parts and Materials Innovation, Yeungnam University, Gyeongsan, 38544, Republic of Korea

    M. S. Mekala

  4. Decentralized Science Lab (DSL), Department of Software Engineering & Game Development, College of Computing and Software Engineering, Kennesaw State University, 680 Arntson Drive, Marietta, 30060, USA

    Patan Rizwan

  5. Department of Computer Science and Engineering, Sree Vidyanikethan Engineering College, Tirupati, AP, India

    Suresh Kallam

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  1. K. Sathish
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  2. Y. V. Narayana
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Correspondence to M. S. Mekala or Patan Rizwan.

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Sathish, K., Narayana, Y.V., Mekala, M.S. et al. Efficient tumor volume measurement and segmentation approach for CT image based on twin support vector machines. Neural Comput & Applic 34, 7199–7207 (2022). https://doi.org/10.1007/s00521-021-06769-y

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  • DOI: https://doi.org/10.1007/s00521-021-06769-y

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