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Machine Learning Based Approach for Automated Cervical Dysplasia Detection Using Multi-Resolution Transform Domain Features

Author

Listed:
  • Kangkana Bora

    (Department of Computer Science and Information Technology, Cotton University, Guwahati 781001, India)

  • Lipi B. Mahanta

    (Institute of Advanced Study in Science and Technology, Guwahati 781035, India)

  • Kasmika Borah

    (Department of Computer Science and Information Technology, Cotton University, Guwahati 781001, India)

  • Genevieve Chyrmang

    (Department of Computer Science and Information Technology, Cotton University, Guwahati 781001, India)

  • Barun Barua

    (Department of Computer Science and Information Technology, Cotton University, Guwahati 781001, India)

  • Saurav Mallik

    (Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
    Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA)

  • Himanish Shekhar Das

    (Department of Computer Science and Information Technology, Cotton University, Guwahati 781001, India)

  • Zhongming Zhao

    (Center for Precision Health, School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
    Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA
    Department of Pathology and Laboratory Medicine, McGovern Medical School, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA)

Abstract

Pattern detection and classification of cervical cell dysplasia can assist with diagnosis and treatment. This study aims to develop a computational model for real-world applications for cervical dysplasia that has the highest degree of accuracy and the lowest computation time. Initially, an ML framework is created, which has been trained and evaluated to classify dysplasia. Three different color models, three multi-resolution transform-based techniques for feature extraction (each with different filters), two feature representation schemes, and two well-known classification approaches are developed in conjunction to determine the optimal combination of “transform (filter) ⇒ color model ⇒ feature representation ⇒ classifier”. Extensive evaluations of two datasets, one is indigenous (own generated database) and the other is publicly available, demonstrated that the Non-subsampled Contourlet Transform (NSCT) feature-based classification performs well, it reveals that the combination “NSCT (pyrexc,pkva), YCbCr, MLP” gives most satisfactory framework with a classification accuracy of 98.02% (average) using the F1 feature set. Compared to two other approaches, our proposed model yields the most satisfying results, with an accuracy in the range of 98.00–99.50%.

Suggested Citation

  • Kangkana Bora & Lipi B. Mahanta & Kasmika Borah & Genevieve Chyrmang & Barun Barua & Saurav Mallik & Himanish Shekhar Das & Zhongming Zhao, 2022. "Machine Learning Based Approach for Automated Cervical Dysplasia Detection Using Multi-Resolution Transform Domain Features," Mathematics, MDPI, vol. 10(21), pages 1-11, November.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:21:p:4126-:d:963939
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    Citations

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    Cited by:

    1. Anjan Bandyopadhyay & Ansh Sarkar & Sujata Swain & Debajyoty Banik & Aboul Ella Hassanien & Saurav Mallik & Aimin Li & Hong Qin, 2023. "A Game-Theoretic Approach for Rendering Immersive Experiences in the Metaverse," Mathematics, MDPI, vol. 11(6), pages 1-22, March.
    2. Brijit Bhattacharjee & Bikash Debnath & Jadav Chandra Das & Subhashis Kar & Nandan Banerjee & Saurav Mallik & Debashis De, 2023. "Predicting the Future Appearances of Lost Children for Information Forensics with Adaptive Discriminator-Based FLM GAN," Mathematics, MDPI, vol. 11(6), pages 1-19, March.

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