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Segmentation of Cervical Cancer by OLHT Based DT-CWT Techniques

P. R. Sheebha Rani1,*, R. Jemila Rose2

1 Department of Computer Science and Engineering, St. Xavier’s Catholic College of Engineering, Nagercoil, 629003, Tamilnadu, India
2 Department of Computer Science and Engineering, St. Xavier’s Catholic College of Engineering, Chunkankadai, Nagercoil, 629003, Tamilnadu, India

* Corresponding Author: P. R. Sheebha Rani. Email:

Intelligent Automation & Soft Computing 2022, 33(3), 1579-1592. https://doi.org/10.32604/iasc.2022.023587

Abstract

Every year, cervical cancer (CC) is the leading cause of death in women around the world. If detected early enough, this cancer can be treated, and patients will receive adequate care. This study introduces a novel ultrasound-based method for detecting CC. The Oriented Local Histogram Technique (OLHT) is used to improve the image corners in the cervical image (CI), and the Dual-Tree Complex Wavelet Transform (DT-CWT) is used to build a multi-resolution image (CI). Wavelet, and Local Binary Pattern are among the elements retrieved from this improved multi-resolution CI (LBP). The retrieved appearance is trained and tested using a feed-forward propagation neural network, and the ANFIS classifier is utilized to classify them. The purpose of this classifier is to distinguish between normal and pathological cervical pictures. Sensitivity is 97.52 percent, specificity is 99.46 percent, accuracy is 98.39 percent, precision is 97.48 percent, PPV is 97.38 percent, NPV is 92.27 percent, LRP is 141.81 percent, 0.0946 percent LRN, FPR is 96.82 percent, and NPR is 91.46 percent for the CC detection categorization. The proposed methodology outperforms standard CC identification and classification methodologies.

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Cite This Article

P. R. Sheebha Rani and R. Jemila Rose, "Segmentation of cervical cancer by olht based dt-cwt techniques," Intelligent Automation & Soft Computing, vol. 33, no.3, pp. 1579–1592, 2022.



This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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