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Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection

Percentage mammographic breast density (MBD) is one of the most notable biomarkers. It is assessed visually with the support of radiologists with the four qualitative Breast Imaging Reporting and Data System (BIRADS) categories. It is demanding for radiologists to differentiate between the two varia...

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Autores principales: Pawar, Shivaji D., Sharma, Kamal K., Sapate, Suhas G., Yadav, Geetanjali Y., Alroobaea, Roobaea, Alzahrani, Sabah M., Hedabou, Mustapha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9081505/
https://www.ncbi.nlm.nih.gov/pubmed/35548086
http://dx.doi.org/10.3389/fpubh.2022.885212
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author Pawar, Shivaji D.
Sharma, Kamal K.
Sapate, Suhas G.
Yadav, Geetanjali Y.
Alroobaea, Roobaea
Alzahrani, Sabah M.
Hedabou, Mustapha
author_facet Pawar, Shivaji D.
Sharma, Kamal K.
Sapate, Suhas G.
Yadav, Geetanjali Y.
Alroobaea, Roobaea
Alzahrani, Sabah M.
Hedabou, Mustapha
author_sort Pawar, Shivaji D.
collection PubMed
description Percentage mammographic breast density (MBD) is one of the most notable biomarkers. It is assessed visually with the support of radiologists with the four qualitative Breast Imaging Reporting and Data System (BIRADS) categories. It is demanding for radiologists to differentiate between the two variably allocated BIRADS classes, namely, “BIRADS C and BIRADS D.” Recently, convolution neural networks have been found superior in classification tasks due to their ability to extract local features with shared weight architecture and space invariance characteristics. The proposed study intends to examine an artificial intelligence (AI)-based MBD classifier toward developing a latent computer-assisted tool for radiologists to distinguish the BIRADS class in modern clinical progress. This article proposes a multichannel DenseNet architecture for MBD classification. The proposed architecture consists of four-channel DenseNet transfer learning architecture to extract significant features from a single patient's two a mediolateral oblique (MLO) and two craniocaudal (CC) views of digital mammograms. The performance of the proposed classifier is evaluated using 200 cases consisting of 800 digital mammograms of the different BIRADS density classes with validated density ground truth. The classifier's performance is assessed with quantitative metrics such as precision, responsiveness, specificity, and the area under the curve (AUC). The concluding preliminary outcomes reveal that this intended multichannel model has delivered good performance with an accuracy of 96.67% during training and 90.06% during testing and an average AUC of 0.9625. Obtained results are also validated qualitatively with the help of a radiologist expert in the field of MBD. Proposed architecture achieved state-of-the-art results with a fewer number of images and with less computation power.
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spelling pubmed-90815052022-05-10 Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection Pawar, Shivaji D. Sharma, Kamal K. Sapate, Suhas G. Yadav, Geetanjali Y. Alroobaea, Roobaea Alzahrani, Sabah M. Hedabou, Mustapha Front Public Health Public Health Percentage mammographic breast density (MBD) is one of the most notable biomarkers. It is assessed visually with the support of radiologists with the four qualitative Breast Imaging Reporting and Data System (BIRADS) categories. It is demanding for radiologists to differentiate between the two variably allocated BIRADS classes, namely, “BIRADS C and BIRADS D.” Recently, convolution neural networks have been found superior in classification tasks due to their ability to extract local features with shared weight architecture and space invariance characteristics. The proposed study intends to examine an artificial intelligence (AI)-based MBD classifier toward developing a latent computer-assisted tool for radiologists to distinguish the BIRADS class in modern clinical progress. This article proposes a multichannel DenseNet architecture for MBD classification. The proposed architecture consists of four-channel DenseNet transfer learning architecture to extract significant features from a single patient's two a mediolateral oblique (MLO) and two craniocaudal (CC) views of digital mammograms. The performance of the proposed classifier is evaluated using 200 cases consisting of 800 digital mammograms of the different BIRADS density classes with validated density ground truth. The classifier's performance is assessed with quantitative metrics such as precision, responsiveness, specificity, and the area under the curve (AUC). The concluding preliminary outcomes reveal that this intended multichannel model has delivered good performance with an accuracy of 96.67% during training and 90.06% during testing and an average AUC of 0.9625. Obtained results are also validated qualitatively with the help of a radiologist expert in the field of MBD. Proposed architecture achieved state-of-the-art results with a fewer number of images and with less computation power. Frontiers Media S.A. 2022-04-25 /pmc/articles/PMC9081505/ /pubmed/35548086 http://dx.doi.org/10.3389/fpubh.2022.885212 Text en Copyright © 2022 Pawar, Sharma, Sapate, Yadav, Alroobaea, Alzahrani and Hedabou. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Public Health
Pawar, Shivaji D.
Sharma, Kamal K.
Sapate, Suhas G.
Yadav, Geetanjali Y.
Alroobaea, Roobaea
Alzahrani, Sabah M.
Hedabou, Mustapha
Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection
title Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection
title_full Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection
title_fullStr Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection
title_full_unstemmed Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection
title_short Multichannel DenseNet Architecture for Classification of Mammographic Breast Density for Breast Cancer Detection
title_sort multichannel densenet architecture for classification of mammographic breast density for breast cancer detection
topic Public Health
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9081505/
https://www.ncbi.nlm.nih.gov/pubmed/35548086
http://dx.doi.org/10.3389/fpubh.2022.885212
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