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Detection of cervix tumor using an intelligent system accompanied with PNN classification approach

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Abstract

In the medical field, image analysis is used to identify tumors. Image segmentation is a critical aspect of image processing. Pap-smear imaging techniques are one of the tools to diagnose the cervix cancer and to detect and identify the malignant and benign tissue in the human body. Here, in this research article, the experimental analysis works the input images are taken from the public Herlev dataset. The proposed work for cervix cancer detection and classification was carried out in four phases in which the first phase was the selection of suitable algorithm for denoising and contrast enhancement the Pap smear image. First, the image is preprocesses using the asymmetrical triangular function with a moving average fuzzy filter. The next step, canny edge detection and a boundary monitoring algorithm are used to isolate the cervix. The adaptive concave-hull algorithm is used to fix the cervical boundary. Then Otsu thresholding based Fractional-Order Darwinian Particle Swarm Optimization concept is used for segmentation of tumor contour. The combination function is then used to derive features such as gray-level co-occurrence matrix (GLCM), FOS, and structural features dependent on the tumor region for the segmented tumor region. Finally, the features are extracted, and then the image is classified as a benign or malignant cervix using the PNN (probabilistic neural network) classifier function. The sensitivity, specificity, and accuracy of the various evaluation standards are used to validate the proposed procedure. At the end of the work, this approach is well correlated with other state-of-the-art approaches on the same image open-access dataset.

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Acknowledgements

First of all, the writers are thankful for the continuing encouragement and support from the management of the Udaya School of Engineering for their study. The public available cervix cancer scans database used in this study. Finally, we want to thank the anonymous reviewers for their help with this article improvement.

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Authors and Affiliations

  1. Department of ECE, Udaya School of Engineering, Kanyakumari, Tamil Nadu, India

    K. Vijila Rani

  2. Physics Department, S.T. Hindu College, Nagercoil, Tamil Nadu, India

    M. Eugine Prince

  3. Department of EEE, Noorul Islam Centre for Higher Education, Kumarakovil, Tamil Nadu, India

    P. Sujatha Therese

  4. Artificial Intelligence and Data Science, R.M.K College of Engineering and Technology, Thiruvallur, Tamil Nadu, India

    P. Josephin Shermila

  5. Department of ECE, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India

    E. Anna Devi

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  4. P. Josephin Shermila
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Contributions

KVR contributed to conceptualization, methodology, validation, visualization, writing—original draft, and writing—reviewer comments correction. MEP contributed to visualization, data correction, resources, and validation. PST contributed to software selection, validation, and visualization. PJS contributed to writing—original draft and reviewer comments correction and editing. EAD contributed to proof reading and visualization.

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Correspondence to K. Vijila Rani.

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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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Vijila Rani, K., Eugine Prince, M., Sujatha Therese, P. et al. Detection of cervix tumor using an intelligent system accompanied with PNN classification approach. SIViP 17, 3873–3881 (2023). https://doi.org/10.1007/s11760-023-02616-w

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  • DOI: https://doi.org/10.1007/s11760-023-02616-w

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