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Automatic Segmentation of Calcification Areas in Digital Breast Images

In this study, the authors hope to demonstrate that when mammography is combined with intelligent segmentation techniques, it can become more effective in diagnosing breast abnormalities and aiding in the early detection of breast cancer. In conjunction with intelligent segmentation techniques, mamm...

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Autores principales: Abdulrazzaq, Ammar Akram, Muhammed, Yasser, Al-Douri, Asaad T., Mohamad, A. A. Hamad, Ibrahim, Abdelrahman Mohamed
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187475/
https://www.ncbi.nlm.nih.gov/pubmed/35692589
http://dx.doi.org/10.1155/2022/2525433
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author Abdulrazzaq, Ammar Akram
Muhammed, Yasser
Al-Douri, Asaad T.
Mohamad, A. A. Hamad
Ibrahim, Abdelrahman Mohamed
author_facet Abdulrazzaq, Ammar Akram
Muhammed, Yasser
Al-Douri, Asaad T.
Mohamad, A. A. Hamad
Ibrahim, Abdelrahman Mohamed
author_sort Abdulrazzaq, Ammar Akram
collection PubMed
description In this study, the authors hope to demonstrate that when mammography is combined with intelligent segmentation techniques, it can become more effective in diagnosing breast abnormalities and aiding in the early detection of breast cancer. In conjunction with intelligent segmentation techniques, mammography can be made more effective in diagnosing breast abnormalities and aiding in the early diagnosis of breast cancer, hence increasing its overall effectiveness. The methodology, which includes some concepts of digital imaging and machine learning techniques, will be described in the following section after a review of the literature on breast cancer (categories, prevention involving the environment and lifestyle, diagnosis, and tracking of the disease) has been completed (neural networks and random forests). It was possible to achieve these results by working with an image collection that previously had questionable regions (per the given technique). Fiji software extracted problematic candidate regions from mammography images, which were subsequently subjected to further examination. To categorize the results of the picture segmentation, they were sorted into three groups, which were as follows: random forest and neural networks both generated promising results in the segmentation of suspicious parts that were emphasized in the highlight of the image, and this was true for both algorithms. Detection of contours of the regions was carried out, indicating that cuts of these segmented sections may be created. Later on, automatic categorization of the targets can be carried out using a learning algorithm, as illustrated in the experiment.
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spelling pubmed-91874752022-06-11 Automatic Segmentation of Calcification Areas in Digital Breast Images Abdulrazzaq, Ammar Akram Muhammed, Yasser Al-Douri, Asaad T. Mohamad, A. A. Hamad Ibrahim, Abdelrahman Mohamed Biomed Res Int Research Article In this study, the authors hope to demonstrate that when mammography is combined with intelligent segmentation techniques, it can become more effective in diagnosing breast abnormalities and aiding in the early detection of breast cancer. In conjunction with intelligent segmentation techniques, mammography can be made more effective in diagnosing breast abnormalities and aiding in the early diagnosis of breast cancer, hence increasing its overall effectiveness. The methodology, which includes some concepts of digital imaging and machine learning techniques, will be described in the following section after a review of the literature on breast cancer (categories, prevention involving the environment and lifestyle, diagnosis, and tracking of the disease) has been completed (neural networks and random forests). It was possible to achieve these results by working with an image collection that previously had questionable regions (per the given technique). Fiji software extracted problematic candidate regions from mammography images, which were subsequently subjected to further examination. To categorize the results of the picture segmentation, they were sorted into three groups, which were as follows: random forest and neural networks both generated promising results in the segmentation of suspicious parts that were emphasized in the highlight of the image, and this was true for both algorithms. Detection of contours of the regions was carried out, indicating that cuts of these segmented sections may be created. Later on, automatic categorization of the targets can be carried out using a learning algorithm, as illustrated in the experiment. Hindawi 2022-06-03 /pmc/articles/PMC9187475/ /pubmed/35692589 http://dx.doi.org/10.1155/2022/2525433 Text en Copyright © 2022 Ammar Akram Abdulrazzaq et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Abdulrazzaq, Ammar Akram
Muhammed, Yasser
Al-Douri, Asaad T.
Mohamad, A. A. Hamad
Ibrahim, Abdelrahman Mohamed
Automatic Segmentation of Calcification Areas in Digital Breast Images
title Automatic Segmentation of Calcification Areas in Digital Breast Images
title_full Automatic Segmentation of Calcification Areas in Digital Breast Images
title_fullStr Automatic Segmentation of Calcification Areas in Digital Breast Images
title_full_unstemmed Automatic Segmentation of Calcification Areas in Digital Breast Images
title_short Automatic Segmentation of Calcification Areas in Digital Breast Images
title_sort automatic segmentation of calcification areas in digital breast images
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9187475/
https://www.ncbi.nlm.nih.gov/pubmed/35692589
http://dx.doi.org/10.1155/2022/2525433
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