Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review
This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automaticall...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/22/8298 |
| _version_ | 1797547031249551360 |
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| author | Yuliana Jiménez-Gaona María José Rodríguez-Álvarez Vasudevan Lakshminarayanan |
| author_facet | Yuliana Jiménez-Gaona María José Rodríguez-Álvarez Vasudevan Lakshminarayanan |
| author_sort | Yuliana Jiménez-Gaona |
| collection | DOAJ |
| description | This paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies. |
| first_indexed | 2024-03-10T14:38:35Z |
| format | Article |
| id | doaj.art-1e800336516b4d1f958e56715978984f |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T14:38:35Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-1e800336516b4d1f958e56715978984f2023-11-20T21:57:56ZengMDPI AGApplied Sciences2076-34172020-11-011022829810.3390/app10228298Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical ReviewYuliana Jiménez-Gaona0María José Rodríguez-Álvarez1Vasudevan Lakshminarayanan2Departamento de Química y Ciencias Exactas, Universidad Técnica Particular de Loja, San Cayetano Alto s/n CP1101608, Loja, EcuadorInstituto de Instrumentacion para la Imagen Molecular I3M, Universitat Politécnica de Valencia, E-46022 Valencia, SpainTheoretical and Experimental Epistemology Lab, School of Optometry and Vision Science, University of Waterloo, Waterloo, ON N2L3G1, CanadaThis paper provides a critical review of the literature on deep learning applications in breast tumor diagnosis using ultrasound and mammography images. It also summarizes recent advances in computer-aided diagnosis/detection (CAD) systems, which make use of new deep learning methods to automatically recognize breast images and improve the accuracy of diagnoses made by radiologists. This review is based upon published literature in the past decade (January 2010–January 2020), where we obtained around 250 research articles, and after an eligibility process, 59 articles were presented in more detail. The main findings in the classification process revealed that new DL-CAD methods are useful and effective screening tools for breast cancer, thus reducing the need for manual feature extraction. The breast tumor research community can utilize this survey as a basis for their current and future studies.https://www.mdpi.com/2076-3417/10/22/8298breast cancercomputer-aided diagnosisconvolutional neural networksdeep learningmammographyultrasound |
| spellingShingle | Yuliana Jiménez-Gaona María José Rodríguez-Álvarez Vasudevan Lakshminarayanan Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review Applied Sciences breast cancer computer-aided diagnosis convolutional neural networks deep learning mammography ultrasound |
| title | Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review |
| title_full | Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review |
| title_fullStr | Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review |
| title_full_unstemmed | Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review |
| title_short | Deep-Learning-Based Computer-Aided Systems for Breast Cancer Imaging: A Critical Review |
| title_sort | deep learning based computer aided systems for breast cancer imaging a critical review |
| topic | breast cancer computer-aided diagnosis convolutional neural networks deep learning mammography ultrasound |
| url | https://www.mdpi.com/2076-3417/10/22/8298 |
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