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 imag...
| Main Authors: | , , |
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| Format: | Journal article |
| Language: | English |
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Public Library of Science
2022
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| _version_ | 1797107627267719168 |
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| author | Diosan, L Andreica, A Voiculescu, I |
| author_facet | Diosan, L Andreica, A Voiculescu, I |
| author_sort | Diosan, L |
| collection | OXFORD |
| 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.
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| first_indexed | 2024-03-07T07:18:43Z |
| format | Journal article |
| id | oxford-uuid:a78f44b0-725c-40c3-bec4-686f62731c6a |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T07:18:43Z |
| publishDate | 2022 |
| publisher | Public Library of Science |
| record_format | dspace |
| spelling | oxford-uuid:a78f44b0-725c-40c3-bec4-686f62731c6a2022-09-07T09:13:05ZOn the use of multi-objective evolutionary classifiers for breast cancer detectionJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:a78f44b0-725c-40c3-bec4-686f62731c6aEnglishSymplectic ElementsPublic Library of Science2022Diosan, LAndreica, AVoiculescu, IPurpose 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. |
| spellingShingle | Diosan, L Andreica, A Voiculescu, I 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 |
| work_keys_str_mv | AT diosanl ontheuseofmultiobjectiveevolutionaryclassifiersforbreastcancerdetection AT andreicaa ontheuseofmultiobjectiveevolutionaryclassifiersforbreastcancerdetection AT voiculescui ontheuseofmultiobjectiveevolutionaryclassifiersforbreastcancerdetection |