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Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization
Traditional breast cancer detection algorithms require manual extraction of features from mammogram images and professional medical knowledge. Still, the quality of mammogram images hampers this and extracting high–quality features, which can result in very long processing times. Therefore, this pap...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777540/ https://www.ncbi.nlm.nih.gov/pubmed/36553095 http://dx.doi.org/10.3390/diagnostics12123088 |
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author | Wisaeng, Kittipol |
author_facet | Wisaeng, Kittipol |
author_sort | Wisaeng, Kittipol |
collection | PubMed |
description | Traditional breast cancer detection algorithms require manual extraction of features from mammogram images and professional medical knowledge. Still, the quality of mammogram images hampers this and extracting high–quality features, which can result in very long processing times. Therefore, this paper proposes a new K–means++ clustering based on Cuckoo Search Optimization (KM++CSO) for breast cancer detection. The pre-processing method is used to improve the proposed KM++CSO method more segmentation efficiently. Furthermore, the interpretability is further enhanced using mathematical morphology and OTSU’s threshold. To this end, we tested the effectiveness of the KM++CSO methods on the mammogram image analysis society of the Mini–Mammographic Image Analysis Society (Mini–MIAS), the Digital Database for Screening Mammography (DDSM), and the Breast Cancer Digital Repository (BCDR) dataset through cross-validation. We maximize the accuracy and Jaccard index score, which is a measure that indicates the similarity between detected cancer and their corresponding reference cancer regions. The experimental results showed that the detection method obtained an accuracy of 96.42% (Mini–MIAS), 95.49% (DDSM), and 96.92% (BCDR). On overage, the KM++CSO method obtained 96.27% accuracy for three publicly available datasets. In addition, the detection results provided the 91.05% Jaccard index score. |
format | Online Article Text |
id | pubmed-9777540 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-97775402022-12-23 Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization Wisaeng, Kittipol Diagnostics (Basel) Article Traditional breast cancer detection algorithms require manual extraction of features from mammogram images and professional medical knowledge. Still, the quality of mammogram images hampers this and extracting high–quality features, which can result in very long processing times. Therefore, this paper proposes a new K–means++ clustering based on Cuckoo Search Optimization (KM++CSO) for breast cancer detection. The pre-processing method is used to improve the proposed KM++CSO method more segmentation efficiently. Furthermore, the interpretability is further enhanced using mathematical morphology and OTSU’s threshold. To this end, we tested the effectiveness of the KM++CSO methods on the mammogram image analysis society of the Mini–Mammographic Image Analysis Society (Mini–MIAS), the Digital Database for Screening Mammography (DDSM), and the Breast Cancer Digital Repository (BCDR) dataset through cross-validation. We maximize the accuracy and Jaccard index score, which is a measure that indicates the similarity between detected cancer and their corresponding reference cancer regions. The experimental results showed that the detection method obtained an accuracy of 96.42% (Mini–MIAS), 95.49% (DDSM), and 96.92% (BCDR). On overage, the KM++CSO method obtained 96.27% accuracy for three publicly available datasets. In addition, the detection results provided the 91.05% Jaccard index score. MDPI 2022-12-07 /pmc/articles/PMC9777540/ /pubmed/36553095 http://dx.doi.org/10.3390/diagnostics12123088 Text en © 2022 by the author. 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 Wisaeng, Kittipol Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization |
title | Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization |
title_full | Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization |
title_fullStr | Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization |
title_full_unstemmed | Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization |
title_short | Breast Cancer Detection in Mammogram Images Using K–Means++ Clustering Based on Cuckoo Search Optimization |
title_sort | breast cancer detection in mammogram images using k–means++ clustering based on cuckoo search optimization |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9777540/ https://www.ncbi.nlm.nih.gov/pubmed/36553095 http://dx.doi.org/10.3390/diagnostics12123088 |
work_keys_str_mv | AT wisaengkittipol breastcancerdetectioninmammogramimagesusingkmeansclusteringbasedoncuckoosearchoptimization |