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Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer
BACKGROUND: Breast cancer is one of the most common cancer types in the world and is a serious threat to health. This type of cancer is complex; it is a hereditary disease and does not result from a single cause. The diagnosis of cancer starts with a biopsy. Various methods are used to detect and re...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Scientific Literature, Inc.
2018
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154116/ https://www.ncbi.nlm.nih.gov/pubmed/30222727 http://dx.doi.org/10.12659/MSM.910520 |
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author | Toprak, Abdullah |
author_facet | Toprak, Abdullah |
author_sort | Toprak, Abdullah |
collection | PubMed |
description | BACKGROUND: Breast cancer is one of the most common cancer types in the world and is a serious threat to health. This type of cancer is complex; it is a hereditary disease and does not result from a single cause. The diagnosis of cancer starts with a biopsy. Various methods are used to detect and recognize cancer cells, from microscopic images and mammography to ultrasonography and magnetic resonance images (MRI). MATERIAL/METHODS: Detection and characterization of benign and malignant cells by image-processing-based segmentation for breast cancer diagnosis is important for early diagnosis. In the present study, Extreme Learning Machine (ELM) classification was performed for 9 features based on image segmentation in the Breast Cancer Wisconsin (Diagnostic) data set in the UC Irvine Machine Learning Repository database. RESULTS: The results obtained with the developed method were compared with the results of other machine learning methods (Naive Bayes, Support Vector Machine, and Artificial Neural Network) and it showed the highest performance, with a result of 98.99%. CONCLUSIONS: It was found that both accuracy and speed were good. We present a method that can be applied in cell morphology detection and classification in automated systems that classify by computer-aided mammogram image features. |
format | Online Article Text |
id | pubmed-6154116 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | International Scientific Literature, Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61541162018-09-26 Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer Toprak, Abdullah Med Sci Monit Lab/In Vitro Research BACKGROUND: Breast cancer is one of the most common cancer types in the world and is a serious threat to health. This type of cancer is complex; it is a hereditary disease and does not result from a single cause. The diagnosis of cancer starts with a biopsy. Various methods are used to detect and recognize cancer cells, from microscopic images and mammography to ultrasonography and magnetic resonance images (MRI). MATERIAL/METHODS: Detection and characterization of benign and malignant cells by image-processing-based segmentation for breast cancer diagnosis is important for early diagnosis. In the present study, Extreme Learning Machine (ELM) classification was performed for 9 features based on image segmentation in the Breast Cancer Wisconsin (Diagnostic) data set in the UC Irvine Machine Learning Repository database. RESULTS: The results obtained with the developed method were compared with the results of other machine learning methods (Naive Bayes, Support Vector Machine, and Artificial Neural Network) and it showed the highest performance, with a result of 98.99%. CONCLUSIONS: It was found that both accuracy and speed were good. We present a method that can be applied in cell morphology detection and classification in automated systems that classify by computer-aided mammogram image features. International Scientific Literature, Inc. 2018-09-17 /pmc/articles/PMC6154116/ /pubmed/30222727 http://dx.doi.org/10.12659/MSM.910520 Text en © Med Sci Monit, 2018 This work is licensed under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) ) |
spellingShingle | Lab/In Vitro Research Toprak, Abdullah Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer |
title | Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer |
title_full | Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer |
title_fullStr | Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer |
title_full_unstemmed | Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer |
title_short | Extreme Learning Machine (ELM)-Based Classification of Benign and Malignant Cells in Breast Cancer |
title_sort | extreme learning machine (elm)-based classification of benign and malignant cells in breast cancer |
topic | Lab/In Vitro Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6154116/ https://www.ncbi.nlm.nih.gov/pubmed/30222727 http://dx.doi.org/10.12659/MSM.910520 |
work_keys_str_mv | AT toprakabdullah extremelearningmachineelmbasedclassificationofbenignandmalignantcellsinbreastcancer |