Breast Cancer Detection based on 3-D Mammography Images using Deep Learning Strategies
In recent scenario, women are suffering from breast cancer disease across the world. Mammography is one of the important methods to detect breast cancer early; that to reduce the cost and workload of radiologists. Medical image processing is a tremendous technique used to determine the disease in ad...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | fas |
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University of Tehran
2022-07-01
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| Series: | Journal of Information Technology Management |
| Subjects: | |
| Online Access: | https://jitm.ut.ac.ir/article_88132_b5fa3520da1d317b567f63050316231e.pdf |
| _version_ | 1811194530431500288 |
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| author | K. Martin Sagayam A. Amir Anton Jone Korhan Cengiz L. Rajesh Ahmed Elngar |
| author_facet | K. Martin Sagayam A. Amir Anton Jone Korhan Cengiz L. Rajesh Ahmed Elngar |
| author_sort | K. Martin Sagayam |
| collection | DOAJ |
| description | In recent scenario, women are suffering from breast cancer disease across the world. Mammography is one of the important methods to detect breast cancer early; that to reduce the cost and workload of radiologists. Medical image processing is a tremendous technique used to determine the disease in advance to reduce the risk factor. To predict the disease from 2-D mammography images for diagnosing and detecting based on advanced soft computing paradigm. Still, to get more accuracy in all coordinate axes, 3-D mammography imaging is used to capture depth information from all different angles. After the reconstruction of this process, a better quality of 3D mammography is obtained. It is useful for the experts to identify the disease in well advance. To improve the accuracy of disease findings, deep convolution neural networks (CNN) can be applied for automatic feature learning, and classifier building. This work also presents a comparison of the other state of art methods used in the last decades. |
| first_indexed | 2024-04-12T00:29:12Z |
| format | Article |
| id | doaj.art-76beb968ee374e10980aab928de9440c |
| institution | Directory Open Access Journal |
| issn | 2008-5893 2423-5059 |
| language | fas |
| last_indexed | 2024-04-12T00:29:12Z |
| publishDate | 2022-07-01 |
| publisher | University of Tehran |
| record_format | Article |
| series | Journal of Information Technology Management |
| spelling | doaj.art-76beb968ee374e10980aab928de9440c2022-12-22T03:55:24ZfasUniversity of TehranJournal of Information Technology Management2008-58932423-50592022-07-0114421810.22059/jitm.2022.8813288132Breast Cancer Detection based on 3-D Mammography Images using Deep Learning StrategiesK. Martin Sagayam0A. Amir Anton Jone1Korhan Cengiz2L. Rajesh3Ahmed Elngar4Department of ECE, Karunya Institute of Technology and Sciences, Coimbatore - 641114, IndiaDepartment of ECE, Karunya Institute of Technology and Sciences, Coimbatore - 641114, IndiaCollege of Information Technology, University of Fujaiah, UAE.Department of Electronics Engineering, Madras Institute of Technology, Anna University, Chennai.Faculty of Computer & Artificial Intelligence, Beni-Suef University, Beni-Suef City, 62511, Egypt; College of Computer Information Technology, American University in the Emirates, United Arab Emirates.In recent scenario, women are suffering from breast cancer disease across the world. Mammography is one of the important methods to detect breast cancer early; that to reduce the cost and workload of radiologists. Medical image processing is a tremendous technique used to determine the disease in advance to reduce the risk factor. To predict the disease from 2-D mammography images for diagnosing and detecting based on advanced soft computing paradigm. Still, to get more accuracy in all coordinate axes, 3-D mammography imaging is used to capture depth information from all different angles. After the reconstruction of this process, a better quality of 3D mammography is obtained. It is useful for the experts to identify the disease in well advance. To improve the accuracy of disease findings, deep convolution neural networks (CNN) can be applied for automatic feature learning, and classifier building. This work also presents a comparison of the other state of art methods used in the last decades.https://jitm.ut.ac.ir/article_88132_b5fa3520da1d317b567f63050316231e.pdfbreast cancermammographyradiologistscaddeep learningconvolutional neural networkmedical imaging |
| spellingShingle | K. Martin Sagayam A. Amir Anton Jone Korhan Cengiz L. Rajesh Ahmed Elngar Breast Cancer Detection based on 3-D Mammography Images using Deep Learning Strategies Journal of Information Technology Management breast cancer mammography radiologists cad deep learning convolutional neural network medical imaging |
| title | Breast Cancer Detection based on 3-D Mammography Images using Deep Learning Strategies |
| title_full | Breast Cancer Detection based on 3-D Mammography Images using Deep Learning Strategies |
| title_fullStr | Breast Cancer Detection based on 3-D Mammography Images using Deep Learning Strategies |
| title_full_unstemmed | Breast Cancer Detection based on 3-D Mammography Images using Deep Learning Strategies |
| title_short | Breast Cancer Detection based on 3-D Mammography Images using Deep Learning Strategies |
| title_sort | breast cancer detection based on 3 d mammography images using deep learning strategies |
| topic | breast cancer mammography radiologists cad deep learning convolutional neural network medical imaging |
| url | https://jitm.ut.ac.ir/article_88132_b5fa3520da1d317b567f63050316231e.pdf |
| work_keys_str_mv | AT kmartinsagayam breastcancerdetectionbasedon3dmammographyimagesusingdeeplearningstrategies AT aamirantonjone breastcancerdetectionbasedon3dmammographyimagesusingdeeplearningstrategies AT korhancengiz breastcancerdetectionbasedon3dmammographyimagesusingdeeplearningstrategies AT lrajesh breastcancerdetectionbasedon3dmammographyimagesusingdeeplearningstrategies AT ahmedelngar breastcancerdetectionbasedon3dmammographyimagesusingdeeplearningstrategies |