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Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection
Every year, cervical cancer is a leading cause of mortality in women all over the world. This cancer can be cured if it is detected early and patients are treated promptly. This study proposes a new strategy for the detection of cervical cancer using cervigram pictures. The associated histogram equa...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914420/ https://www.ncbi.nlm.nih.gov/pubmed/36766652 http://dx.doi.org/10.3390/diagnostics13030548 |
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author | Srinivasan, Saravanan Raju, Aravind Britto Karuppanan Mathivanan, Sandeep Kumar Jayagopal, Prabhu Babu, Jyothi Chinna Sahu, Aditya Kumar |
author_facet | Srinivasan, Saravanan Raju, Aravind Britto Karuppanan Mathivanan, Sandeep Kumar Jayagopal, Prabhu Babu, Jyothi Chinna Sahu, Aditya Kumar |
author_sort | Srinivasan, Saravanan |
collection | PubMed |
description | Every year, cervical cancer is a leading cause of mortality in women all over the world. This cancer can be cured if it is detected early and patients are treated promptly. This study proposes a new strategy for the detection of cervical cancer using cervigram pictures. The associated histogram equalization (AHE) technique is used to improve the edges of the cervical image, and then the finite ridgelet transform is used to generate a multi-resolution picture. Then, from this converted multi-resolution cervical picture, features such as ridgelets, gray-level run-length matrices, moment invariant, and enhanced local ternary pattern are retrieved. A feed-forward backward propagation neural network is used to train and test these extracted features in order to classify the cervical images as normal or abnormal. To detect and segment cancer regions, morphological procedures are applied to the abnormal cervical images. The cervical cancer detection system’s performance metrics include 98.11% sensitivity, 98.97% specificity, 99.19% accuracy, a PPV of 98.88%, an NPV of 91.91%, an LPR of 141.02%, an LNR of 0.0836, 98.13% precision, 97.15% FPs, and 90.89% FNs. The simulation outcomes show that the proposed method is better at detecting and segmenting cervical cancer than the traditional methods. |
format | Online Article Text |
id | pubmed-9914420 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-99144202023-02-11 Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection Srinivasan, Saravanan Raju, Aravind Britto Karuppanan Mathivanan, Sandeep Kumar Jayagopal, Prabhu Babu, Jyothi Chinna Sahu, Aditya Kumar Diagnostics (Basel) Article Every year, cervical cancer is a leading cause of mortality in women all over the world. This cancer can be cured if it is detected early and patients are treated promptly. This study proposes a new strategy for the detection of cervical cancer using cervigram pictures. The associated histogram equalization (AHE) technique is used to improve the edges of the cervical image, and then the finite ridgelet transform is used to generate a multi-resolution picture. Then, from this converted multi-resolution cervical picture, features such as ridgelets, gray-level run-length matrices, moment invariant, and enhanced local ternary pattern are retrieved. A feed-forward backward propagation neural network is used to train and test these extracted features in order to classify the cervical images as normal or abnormal. To detect and segment cancer regions, morphological procedures are applied to the abnormal cervical images. The cervical cancer detection system’s performance metrics include 98.11% sensitivity, 98.97% specificity, 99.19% accuracy, a PPV of 98.88%, an NPV of 91.91%, an LPR of 141.02%, an LNR of 0.0836, 98.13% precision, 97.15% FPs, and 90.89% FNs. The simulation outcomes show that the proposed method is better at detecting and segmenting cervical cancer than the traditional methods. MDPI 2023-02-02 /pmc/articles/PMC9914420/ /pubmed/36766652 http://dx.doi.org/10.3390/diagnostics13030548 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Srinivasan, Saravanan Raju, Aravind Britto Karuppanan Mathivanan, Sandeep Kumar Jayagopal, Prabhu Babu, Jyothi Chinna Sahu, Aditya Kumar Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection |
title | Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection |
title_full | Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection |
title_fullStr | Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection |
title_full_unstemmed | Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection |
title_short | Local-Ternary-Pattern-Based Associated Histogram Equalization Technique for Cervical Cancer Detection |
title_sort | local-ternary-pattern-based associated histogram equalization technique for cervical cancer detection |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9914420/ https://www.ncbi.nlm.nih.gov/pubmed/36766652 http://dx.doi.org/10.3390/diagnostics13030548 |
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