Open Access

ARTICLE

Segmentation of Cervical Cancer by OLHT Based DT-CWT Techniques

P. R. Sheebha Rani^1,*, 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

Received 13 August 2021; Accepted 14 October 2021; Issue published 24 March 2022

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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Keywords

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