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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...

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Detalles Bibliográficos
Autores principales: Huang, Mei-Ling, Lin, Ting-Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
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.
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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
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