Cargando…
On the use of multi–objective evolutionary classifiers for breast cancer detection
PURPOSE: Breast cancer is one of the most common tumours in women, nevertheless, it is also one of the cancers that is most usually treated. As a result, early detection is critical, which can be accomplished by routine mammograms. This paper aims to describe, analyze, compare and evaluate three ima...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9295958/ https://www.ncbi.nlm.nih.gov/pubmed/35853014 http://dx.doi.org/10.1371/journal.pone.0269950 |
_version_ | 1784750163320897536 |
---|---|
author | Dioşan, Laura Andreica, Anca Voiculescu, Irina |
author_facet | Dioşan, Laura Andreica, Anca Voiculescu, Irina |
author_sort | Dioşan, Laura |
collection | PubMed |
description | PURPOSE: Breast cancer is one of the most common tumours in women, nevertheless, it is also one of the cancers that is most usually treated. As a result, early detection is critical, which can be accomplished by routine mammograms. This paper aims to describe, analyze, compare and evaluate three image descriptors involved in classifying breast cancer images from four databases. APPROACH: Multi–Objective Evolutionary Algorithms (MOEAs) prove themselves as being efficient methods for selection and classification problems. This paper aims to study combinations of well–known classification objectives in order to compare the results of their application in solving very specific learning problems. The experimental results undergo empirical analysis which is supported by a statistical approach. The results are illustrated on a collection of medical image databases, but with a focus on the MOEAs’ performance in terms of several well–known measures. The databases were chosen specifically to feature reliable human annotations, so as to measure the correlation between the gold standard classifications and the various MOEA classifications. RESULTS: We have seen how different statistical tests rank one algorithm over the others in our set as being better. These findings are unsurprising, revealing that there is no single gold standard for comparing diverse techniques or evolutionary algorithms. Furthermore, building meta-classifiers and evaluating them using a single, favorable metric is both extremely unwise and unsatisfactory, as the impact is to skew the results. CONCLUSIONS: The best method to address these flaws is to select the right set of objectives and criteria. Using accuracy-related objectives, for example, is directly linked to maximizing the number of true positives. If, on the other hand, accuracy is chosen as the generic metric, the primary classification goal is shifted to increasing the positively categorized data points. |
format | Online Article Text |
id | pubmed-9295958 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-92959582022-07-20 On the use of multi–objective evolutionary classifiers for breast cancer detection Dioşan, Laura Andreica, Anca Voiculescu, Irina PLoS One Research Article PURPOSE: Breast cancer is one of the most common tumours in women, nevertheless, it is also one of the cancers that is most usually treated. As a result, early detection is critical, which can be accomplished by routine mammograms. This paper aims to describe, analyze, compare and evaluate three image descriptors involved in classifying breast cancer images from four databases. APPROACH: Multi–Objective Evolutionary Algorithms (MOEAs) prove themselves as being efficient methods for selection and classification problems. This paper aims to study combinations of well–known classification objectives in order to compare the results of their application in solving very specific learning problems. The experimental results undergo empirical analysis which is supported by a statistical approach. The results are illustrated on a collection of medical image databases, but with a focus on the MOEAs’ performance in terms of several well–known measures. The databases were chosen specifically to feature reliable human annotations, so as to measure the correlation between the gold standard classifications and the various MOEA classifications. RESULTS: We have seen how different statistical tests rank one algorithm over the others in our set as being better. These findings are unsurprising, revealing that there is no single gold standard for comparing diverse techniques or evolutionary algorithms. Furthermore, building meta-classifiers and evaluating them using a single, favorable metric is both extremely unwise and unsatisfactory, as the impact is to skew the results. CONCLUSIONS: The best method to address these flaws is to select the right set of objectives and criteria. Using accuracy-related objectives, for example, is directly linked to maximizing the number of true positives. If, on the other hand, accuracy is chosen as the generic metric, the primary classification goal is shifted to increasing the positively categorized data points. Public Library of Science 2022-07-19 /pmc/articles/PMC9295958/ /pubmed/35853014 http://dx.doi.org/10.1371/journal.pone.0269950 Text en © 2022 Dioşan et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Dioşan, Laura Andreica, Anca Voiculescu, Irina On the use of multi–objective evolutionary classifiers for breast cancer detection |
title | On the use of multi–objective evolutionary classifiers for breast cancer detection |
title_full | On the use of multi–objective evolutionary classifiers for breast cancer detection |
title_fullStr | On the use of multi–objective evolutionary classifiers for breast cancer detection |
title_full_unstemmed | On the use of multi–objective evolutionary classifiers for breast cancer detection |
title_short | On the use of multi–objective evolutionary classifiers for breast cancer detection |
title_sort | on the use of multi–objective evolutionary classifiers for breast cancer detection |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9295958/ https://www.ncbi.nlm.nih.gov/pubmed/35853014 http://dx.doi.org/10.1371/journal.pone.0269950 |
work_keys_str_mv | AT diosanlaura ontheuseofmultiobjectiveevolutionaryclassifiersforbreastcancerdetection AT andreicaanca ontheuseofmultiobjectiveevolutionaryclassifiersforbreastcancerdetection AT voiculescuirina ontheuseofmultiobjectiveevolutionaryclassifiersforbreastcancerdetection |