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Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach

Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally. Breast cancer detection needs accurate mammography interpretation and analysis, which is challenging for radiologists owing to the intricate anatomy of the breast and low image quality. Advances in deep le...

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Autores principales: Mahmood, Tariq, Li, Jianqiang, Pei, Yan, Akhtar, Faheem, Rehman, Mujeeb Ur, Wasti, Shahbaz Hassan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8794221/
https://www.ncbi.nlm.nih.gov/pubmed/35085352
http://dx.doi.org/10.1371/journal.pone.0263126
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author Mahmood, Tariq
Li, Jianqiang
Pei, Yan
Akhtar, Faheem
Rehman, Mujeeb Ur
Wasti, Shahbaz Hassan
author_facet Mahmood, Tariq
Li, Jianqiang
Pei, Yan
Akhtar, Faheem
Rehman, Mujeeb Ur
Wasti, Shahbaz Hassan
author_sort Mahmood, Tariq
collection PubMed
description Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally. Breast cancer detection needs accurate mammography interpretation and analysis, which is challenging for radiologists owing to the intricate anatomy of the breast and low image quality. Advances in deep learning-based models have significantly improved breast lesions’ detection, localization, risk assessment, and categorization. This study proposes a novel deep learning-based convolutional neural network (ConvNet) that significantly reduces human error in diagnosing breast malignancy tissues. Our methodology is most effective in eliciting task-specific features, as feature learning is coupled with classification tasks to achieve higher performance in automatically classifying the suspicious regions in mammograms as benign and malignant. To evaluate the model’s validity, 322 raw mammogram images from Mammographic Image Analysis Society (MIAS) and 580 from Private datasets were obtained to extract in-depth features, the intensity of information, and the high likelihood of malignancy. Both datasets are magnificently improved through preprocessing, synthetic data augmentation, and transfer learning techniques to attain the distinctive combination of breast tumors. The experimental findings indicate that the proposed approach achieved remarkable training accuracy of 0.98, test accuracy of 0.97, high sensitivity of 0.99, and an AUC of 0.99 in classifying breast masses on mammograms. The developed model achieved promising performance that helps the clinician in the speedy computation of mammography, breast masses diagnosis, treatment planning, and follow-up of disease progression. Moreover, it has the immense potential over retrospective approaches in consistency feature extraction and precise lesions classification.
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spelling pubmed-87942212022-01-28 Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach Mahmood, Tariq Li, Jianqiang Pei, Yan Akhtar, Faheem Rehman, Mujeeb Ur Wasti, Shahbaz Hassan PLoS One Research Article Breast cancer is one of the worst illnesses, with a higher fatality rate among women globally. Breast cancer detection needs accurate mammography interpretation and analysis, which is challenging for radiologists owing to the intricate anatomy of the breast and low image quality. Advances in deep learning-based models have significantly improved breast lesions’ detection, localization, risk assessment, and categorization. This study proposes a novel deep learning-based convolutional neural network (ConvNet) that significantly reduces human error in diagnosing breast malignancy tissues. Our methodology is most effective in eliciting task-specific features, as feature learning is coupled with classification tasks to achieve higher performance in automatically classifying the suspicious regions in mammograms as benign and malignant. To evaluate the model’s validity, 322 raw mammogram images from Mammographic Image Analysis Society (MIAS) and 580 from Private datasets were obtained to extract in-depth features, the intensity of information, and the high likelihood of malignancy. Both datasets are magnificently improved through preprocessing, synthetic data augmentation, and transfer learning techniques to attain the distinctive combination of breast tumors. The experimental findings indicate that the proposed approach achieved remarkable training accuracy of 0.98, test accuracy of 0.97, high sensitivity of 0.99, and an AUC of 0.99 in classifying breast masses on mammograms. The developed model achieved promising performance that helps the clinician in the speedy computation of mammography, breast masses diagnosis, treatment planning, and follow-up of disease progression. Moreover, it has the immense potential over retrospective approaches in consistency feature extraction and precise lesions classification. Public Library of Science 2022-01-27 /pmc/articles/PMC8794221/ /pubmed/35085352 http://dx.doi.org/10.1371/journal.pone.0263126 Text en © 2022 Mahmood et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Mahmood, Tariq
Li, Jianqiang
Pei, Yan
Akhtar, Faheem
Rehman, Mujeeb Ur
Wasti, Shahbaz Hassan
Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
title Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
title_full Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
title_fullStr Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
title_full_unstemmed Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
title_short Breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
title_sort breast lesions classifications of mammographic images using a deep convolutional neural network-based approach
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8794221/
https://www.ncbi.nlm.nih.gov/pubmed/35085352
http://dx.doi.org/10.1371/journal.pone.0263126
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