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Dataset of breast mammography images with masses
Among many cancers, breast cancer is the second most common cause of death in women. Early detection and early treatment reduce breast cancer mortality. Mammography plays an important role in breast cancer screening because it can detect early breast masses or calcification region. One of the drawba...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334406/ https://www.ncbi.nlm.nih.gov/pubmed/32642525 http://dx.doi.org/10.1016/j.dib.2020.105928 |
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author | Huang, Mei-Ling Lin, Ting-Yu |
author_facet | Huang, Mei-Ling Lin, Ting-Yu |
author_sort | Huang, Mei-Ling |
collection | PubMed |
description | Among many cancers, breast cancer is the second most common cause of death in women. Early detection and early treatment reduce breast cancer mortality. Mammography plays an important role in breast cancer screening because it can detect early breast masses or calcification region. One of the drawbacks in breast mammography is breast cancer masses are more difficult to be found in extremely dense breast tissue. We select 106 breast mammography images with masses from INbreast database. Through data augmentation, the number of breast mammography images was increased to 7632. We utilize data augmentation on breast mammography images, and then apply the Convolutional Neural Networks (CNN) models including AlexNet, DenseNet, and ShuffleNet to classify these breast mammography images. |
format | Online Article Text |
id | pubmed-7334406 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-73344062020-07-07 Dataset of breast mammography images with masses Huang, Mei-Ling Lin, Ting-Yu Data Brief Computer Science Among many cancers, breast cancer is the second most common cause of death in women. Early detection and early treatment reduce breast cancer mortality. Mammography plays an important role in breast cancer screening because it can detect early breast masses or calcification region. One of the drawbacks in breast mammography is breast cancer masses are more difficult to be found in extremely dense breast tissue. We select 106 breast mammography images with masses from INbreast database. Through data augmentation, the number of breast mammography images was increased to 7632. We utilize data augmentation on breast mammography images, and then apply the Convolutional Neural Networks (CNN) models including AlexNet, DenseNet, and ShuffleNet to classify these breast mammography images. Elsevier 2020-06-25 /pmc/articles/PMC7334406/ /pubmed/32642525 http://dx.doi.org/10.1016/j.dib.2020.105928 Text en © 2020 Published by Elsevier Inc. http://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). |
spellingShingle | Computer Science Huang, Mei-Ling Lin, Ting-Yu Dataset of breast mammography images with masses |
title | Dataset of breast mammography images with masses |
title_full | Dataset of breast mammography images with masses |
title_fullStr | Dataset of breast mammography images with masses |
title_full_unstemmed | Dataset of breast mammography images with masses |
title_short | Dataset of breast mammography images with masses |
title_sort | dataset of breast mammography images with masses |
topic | Computer Science |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7334406/ https://www.ncbi.nlm.nih.gov/pubmed/32642525 http://dx.doi.org/10.1016/j.dib.2020.105928 |
work_keys_str_mv | AT huangmeiling datasetofbreastmammographyimageswithmasses AT lintingyu datasetofbreastmammographyimageswithmasses |