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Lung tumor analysis using a thrice novelty block classification approach

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Abstract

Nowadays, lung cancer has arisen as one of the major causes of death and subsequently making its detection immensely difficult. In this research article which consists of five steps framework, three different methods were developed for automatic detection and classification of lung tumor in CT (Computed Tomography) images. The initial step is an image acquisition; here, the input images are collected from public and in-house clinical lung cancer image. The next step image enhancement is performed using WFUM (Weiner Filter with Unsharp masking) enhancement technique which can eradicate the noise discern in the input images. In the subsequent step, the HRWBM (Hierarchical Random Walker with Bayes Model) segmentation algorithm is implemented on an enhanced image sequence for lung tumor region prediction and then the features are extracted using GLCM (Gray Level Co-occurrence Matrix). Ultimately, the lung cancer images (Public LIDC database) are classified by utilizing an HRWBM with SVM (Support Vector Machine) classification where the accuracy is 77.8%; in HRWBM with FFNN (Feed-Forward Neural Network) classification, the accuracy is 93.3%; in HRWBM with DRNN (Deep Recurrent Neural Network) classification, the accuracy is 97.3%. For in-house clinical dataset, the classification result is HRWBM with SVM classification where the accuracy is 84%; in HRWBM with FFNN classification, the accuracy is 90%; in HRWBM with DRNN classification, the accuracy is 94.7% predicted. The classification result reveals that among the three algorithms, the third method improves the accurate identification of lung cancer.

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Acknowledgements

The authors are thankful for the National Cancer Institute Kanyakumari for providing the lung cancer scans database and we want to thank the anonymous reviewers for their help with this article improvement.

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Authors and Affiliations

  1. Department of CSE at St.Xavier’s Catholic College of Engineering, Chunkankadai- Nagercoil, India

    S. L. Soniya

  2. Department of ECE at PSN College of Engineering and Technology, Tirunelveli, India

    T. Ajith Bosco Raj

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  1. S. L. Soniya
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  2. T. Ajith Bosco Raj
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Contributions

SLS: Roles: Conceptualization, Methodology, Validation, Visualization, Writing—original draft, Writing-Reviewer Comments Correction, Proof reading and Visualization. TABR: Roles: Visualization, Data Correction, Resources, and Validation Reviewer Comments Correction and Editing.

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Correspondence to S. L. Soniya.

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This study has not been supported by any industrial company and does not serve to promote any commercial product. Anonymized publicly available databases were used in the conducted experiments.

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Soniya, S.L., Raj, T.A.B. Lung tumor analysis using a thrice novelty block classification approach. SIViP 17, 3027–3034 (2023). https://doi.org/10.1007/s11760-023-02523-0

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  • DOI: https://doi.org/10.1007/s11760-023-02523-0

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