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Boosting Breast Cancer Detection Using Convolutional Neural Network
Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast canc...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041556/ https://www.ncbi.nlm.nih.gov/pubmed/33884157 http://dx.doi.org/10.1155/2021/5528622 |
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author | Alanazi, Saad Awadh Kamruzzaman, M. M. Islam Sarker, Md Nazirul Alruwaili, Madallah Alhwaiti, Yousef Alshammari, Nasser Siddiqi, Muhammad Hameed |
author_facet | Alanazi, Saad Awadh Kamruzzaman, M. M. Islam Sarker, Md Nazirul Alruwaili, Madallah Alhwaiti, Yousef Alshammari, Nasser Siddiqi, Muhammad Hameed |
author_sort | Alanazi, Saad Awadh |
collection | PubMed |
description | Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs). The paper investigates the proposed system that uses various convolutional neural network (CNN) architectures to automatically detect breast cancer, comparing the results with those from machine learning (ML) algorithms. All architectures were guided by a big dataset of about 275,000, 50 × 50-pixel RGB image patches. Validation tests were done for quantitative results using the performance measures for every methodology. The proposed system is found to be successful, achieving results with 87% accuracy, which could reduce human mistakes in the diagnosis process. Moreover, our proposed system achieves accuracy higher than the 78% accuracy of machine learning (ML) algorithms. The proposed system therefore improves accuracy by 9% above results from machine learning (ML) algorithms. |
format | Online Article Text |
id | pubmed-8041556 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-80415562021-04-20 Boosting Breast Cancer Detection Using Convolutional Neural Network Alanazi, Saad Awadh Kamruzzaman, M. M. Islam Sarker, Md Nazirul Alruwaili, Madallah Alhwaiti, Yousef Alshammari, Nasser Siddiqi, Muhammad Hameed J Healthc Eng Research Article Breast cancer forms in breast cells and is considered as a very common type of cancer in women. Breast cancer is also a very life-threatening disease of women after lung cancer. A convolutional neural network (CNN) method is proposed in this study to boost the automatic identification of breast cancer by analyzing hostile ductal carcinoma tissue zones in whole-slide images (WSIs). The paper investigates the proposed system that uses various convolutional neural network (CNN) architectures to automatically detect breast cancer, comparing the results with those from machine learning (ML) algorithms. All architectures were guided by a big dataset of about 275,000, 50 × 50-pixel RGB image patches. Validation tests were done for quantitative results using the performance measures for every methodology. The proposed system is found to be successful, achieving results with 87% accuracy, which could reduce human mistakes in the diagnosis process. Moreover, our proposed system achieves accuracy higher than the 78% accuracy of machine learning (ML) algorithms. The proposed system therefore improves accuracy by 9% above results from machine learning (ML) algorithms. Hindawi 2021-04-03 /pmc/articles/PMC8041556/ /pubmed/33884157 http://dx.doi.org/10.1155/2021/5528622 Text en Copyright © 2021 Saad Awadh Alanazi et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article Alanazi, Saad Awadh Kamruzzaman, M. M. Islam Sarker, Md Nazirul Alruwaili, Madallah Alhwaiti, Yousef Alshammari, Nasser Siddiqi, Muhammad Hameed Boosting Breast Cancer Detection Using Convolutional Neural Network |
title | Boosting Breast Cancer Detection Using Convolutional Neural Network |
title_full | Boosting Breast Cancer Detection Using Convolutional Neural Network |
title_fullStr | Boosting Breast Cancer Detection Using Convolutional Neural Network |
title_full_unstemmed | Boosting Breast Cancer Detection Using Convolutional Neural Network |
title_short | Boosting Breast Cancer Detection Using Convolutional Neural Network |
title_sort | boosting breast cancer detection using convolutional neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8041556/ https://www.ncbi.nlm.nih.gov/pubmed/33884157 http://dx.doi.org/10.1155/2021/5528622 |
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