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Automated Breast Cancer Detection Models Based on Transfer Learning

Breast cancer is among the leading causes of mortality for females across the planet. It is essential for the well-being of women to develop early detection and diagnosis techniques. In mammography, focus has contributed to the use of deep learning (DL) models, which have been utilized by radiologis...

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Autores principales: Alruwaili, Madallah, Gouda, Walaa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838322/
https://www.ncbi.nlm.nih.gov/pubmed/35161622
http://dx.doi.org/10.3390/s22030876
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author Alruwaili, Madallah
Gouda, Walaa
author_facet Alruwaili, Madallah
Gouda, Walaa
author_sort Alruwaili, Madallah
collection PubMed
description Breast cancer is among the leading causes of mortality for females across the planet. It is essential for the well-being of women to develop early detection and diagnosis techniques. In mammography, focus has contributed to the use of deep learning (DL) models, which have been utilized by radiologists to enhance the needed processes to overcome the shortcomings of human observers. The transfer learning method is being used to distinguish malignant and benign breast cancer by fine-tuning multiple pre-trained models. In this study, we introduce a framework focused on the principle of transfer learning. In addition, a mixture of augmentation strategies were used to prevent overfitting and produce stable outcomes by increasing the number of mammographic images; including several rotation combinations, scaling, and shifting. On the Mammographic Image Analysis Society (MIAS) dataset, the proposed system was evaluated and achieved an accuracy of 89.5% using (residual network-50) ResNet50, and achieved an accuracy of 70% using the Nasnet-Mobile network. The proposed system demonstrated that pre-trained classification networks are significantly more effective and efficient, making them more acceptable for medical imaging, particularly for small training datasets.
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spelling pubmed-88383222022-02-13 Automated Breast Cancer Detection Models Based on Transfer Learning Alruwaili, Madallah Gouda, Walaa Sensors (Basel) Article Breast cancer is among the leading causes of mortality for females across the planet. It is essential for the well-being of women to develop early detection and diagnosis techniques. In mammography, focus has contributed to the use of deep learning (DL) models, which have been utilized by radiologists to enhance the needed processes to overcome the shortcomings of human observers. The transfer learning method is being used to distinguish malignant and benign breast cancer by fine-tuning multiple pre-trained models. In this study, we introduce a framework focused on the principle of transfer learning. In addition, a mixture of augmentation strategies were used to prevent overfitting and produce stable outcomes by increasing the number of mammographic images; including several rotation combinations, scaling, and shifting. On the Mammographic Image Analysis Society (MIAS) dataset, the proposed system was evaluated and achieved an accuracy of 89.5% using (residual network-50) ResNet50, and achieved an accuracy of 70% using the Nasnet-Mobile network. The proposed system demonstrated that pre-trained classification networks are significantly more effective and efficient, making them more acceptable for medical imaging, particularly for small training datasets. MDPI 2022-01-24 /pmc/articles/PMC8838322/ /pubmed/35161622 http://dx.doi.org/10.3390/s22030876 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Alruwaili, Madallah
Gouda, Walaa
Automated Breast Cancer Detection Models Based on Transfer Learning
title Automated Breast Cancer Detection Models Based on Transfer Learning
title_full Automated Breast Cancer Detection Models Based on Transfer Learning
title_fullStr Automated Breast Cancer Detection Models Based on Transfer Learning
title_full_unstemmed Automated Breast Cancer Detection Models Based on Transfer Learning
title_short Automated Breast Cancer Detection Models Based on Transfer Learning
title_sort automated breast cancer detection models based on transfer learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8838322/
https://www.ncbi.nlm.nih.gov/pubmed/35161622
http://dx.doi.org/10.3390/s22030876
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