Cargando…
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...
Autores principales: | , , , , , , |
---|---|
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 |
_version_ | 1784703000818745344 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9081505 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT pawarshivajid multichanneldensenetarchitectureforclassificationofmammographicbreastdensityforbreastcancerdetection AT sharmakamalk multichanneldensenetarchitectureforclassificationofmammographicbreastdensityforbreastcancerdetection AT sapatesuhasg multichanneldensenetarchitectureforclassificationofmammographicbreastdensityforbreastcancerdetection AT yadavgeetanjaliy multichanneldensenetarchitectureforclassificationofmammographicbreastdensityforbreastcancerdetection AT alroobaearoobaea multichanneldensenetarchitectureforclassificationofmammographicbreastdensityforbreastcancerdetection AT alzahranisabahm multichanneldensenetarchitectureforclassificationofmammographicbreastdensityforbreastcancerdetection AT hedaboumustapha multichanneldensenetarchitectureforclassificationofmammographicbreastdensityforbreastcancerdetection |