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Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization

Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled profess...

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Autores principales: Alhussan, Amel Ali, Abdelhamid, Abdelaziz A., Towfek, S. K., Ibrahim, Abdelhameed, Abualigah, Laith, Khodadadi, Nima, Khafaga, Doaa Sami, Al-Otaibi, Shaha, Ahmed, Ayman Em
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377265/
https://www.ncbi.nlm.nih.gov/pubmed/37504158
http://dx.doi.org/10.3390/biomimetics8030270
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author Alhussan, Amel Ali
Abdelhamid, Abdelaziz A.
Towfek, S. K.
Ibrahim, Abdelhameed
Abualigah, Laith
Khodadadi, Nima
Khafaga, Doaa Sami
Al-Otaibi, Shaha
Ahmed, Ayman Em
author_facet Alhussan, Amel Ali
Abdelhamid, Abdelaziz A.
Towfek, S. K.
Ibrahim, Abdelhameed
Abualigah, Laith
Khodadadi, Nima
Khafaga, Doaa Sami
Al-Otaibi, Shaha
Ahmed, Ayman Em
author_sort Alhussan, Amel Ali
collection PubMed
description Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled professional is always necessary to manually diagnose this malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face several obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, and inadequate training models. In this paper, we developed a novel computationally automated biological mechanism for categorizing breast cancer. Using a new optimization approach based on the Advanced Al-Biruni Earth Radius (ABER) optimization algorithm, a boosting to the classification of breast cancer cases is realized. The stages of the proposed framework include data augmentation, feature extraction using AlexNet based on transfer learning, and optimized classification using a convolutional neural network (CNN). Using transfer learning and optimized CNN for classification improved the accuracy when the results are compared to recent approaches. Two publicly available datasets are utilized to evaluate the proposed framework, and the average classification accuracy is 97.95%. To ensure the statistical significance and difference between the proposed methodology, additional tests are conducted, such as analysis of variance (ANOVA) and Wilcoxon, in addition to evaluating various statistical analysis metrics. The results of these tests emphasized the effectiveness and statistical difference of the proposed methodology compared to current methods.
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spelling pubmed-103772652023-07-29 Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization Alhussan, Amel Ali Abdelhamid, Abdelaziz A. Towfek, S. K. Ibrahim, Abdelhameed Abualigah, Laith Khodadadi, Nima Khafaga, Doaa Sami Al-Otaibi, Shaha Ahmed, Ayman Em Biomimetics (Basel) Article Breast cancer is one of the most common cancers in women, with an estimated 287,850 new cases identified in 2022. There were 43,250 female deaths attributed to this malignancy. The high death rate associated with this type of cancer can be reduced with early detection. Nonetheless, a skilled professional is always necessary to manually diagnose this malignancy from mammography images. Many researchers have proposed several approaches based on artificial intelligence. However, they still face several obstacles, such as overlapping cancerous and noncancerous regions, extracting irrelevant features, and inadequate training models. In this paper, we developed a novel computationally automated biological mechanism for categorizing breast cancer. Using a new optimization approach based on the Advanced Al-Biruni Earth Radius (ABER) optimization algorithm, a boosting to the classification of breast cancer cases is realized. The stages of the proposed framework include data augmentation, feature extraction using AlexNet based on transfer learning, and optimized classification using a convolutional neural network (CNN). Using transfer learning and optimized CNN for classification improved the accuracy when the results are compared to recent approaches. Two publicly available datasets are utilized to evaluate the proposed framework, and the average classification accuracy is 97.95%. To ensure the statistical significance and difference between the proposed methodology, additional tests are conducted, such as analysis of variance (ANOVA) and Wilcoxon, in addition to evaluating various statistical analysis metrics. The results of these tests emphasized the effectiveness and statistical difference of the proposed methodology compared to current methods. MDPI 2023-06-26 /pmc/articles/PMC10377265/ /pubmed/37504158 http://dx.doi.org/10.3390/biomimetics8030270 Text en © 2023 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
Alhussan, Amel Ali
Abdelhamid, Abdelaziz A.
Towfek, S. K.
Ibrahim, Abdelhameed
Abualigah, Laith
Khodadadi, Nima
Khafaga, Doaa Sami
Al-Otaibi, Shaha
Ahmed, Ayman Em
Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization
title Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization
title_full Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization
title_fullStr Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization
title_full_unstemmed Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization
title_short Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization
title_sort classification of breast cancer using transfer learning and advanced al-biruni earth radius optimization
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10377265/
https://www.ncbi.nlm.nih.gov/pubmed/37504158
http://dx.doi.org/10.3390/biomimetics8030270
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