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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...

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Autores principales: Srinivasan, Saravanan, Raju, Aravind Britto Karuppanan, Mathivanan, Sandeep Kumar, Jayagopal, Prabhu, Babu, Jyothi Chinna, Sahu, Aditya Kumar
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
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.
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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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