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A Comprehensive Survey on Deep-Learning-Based Breast Cancer Diagnosis

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Abstract
Breast cancer is now the most frequently diagnosed cancer in women, and its percentage is gradually increasing. Optimistically, there is a good chance of recovery from breast cancer if identified and treated at an early stage. Therefore, several researchers have established deep-learning-based automated methods for their efficiency and accuracy in predicting the growth of cancer cells utilizing medical imaging modalities. As of yet, few review studies on breast cancer diagnosis are available that summarize some existing studies. However, these studies were unable to address emerging architectures and modalities in breast cancer diagnosis. This review focuses on the evolving architectures of deep learning for breast cancer detection. In what follows, this survey presents existing deep-learning-based architectures, analyzes the strengths and limitations of the existing studies, examines the used datasets, and reviews image pre-processing techniques. Furthermore, a concrete review of diverse imaging modalities, performance metrics and results, challenges, and research directions for future researchers is presented.
Author(s)
Muhammad Firoz MridhaMuhammadMd. Abdul HamidMuhammad Mostafa MonowarAshfia Jannat KeyaAbu Quwsar OhiMd. Rashedul Islam김종면
Issued Date
2021
Type
Article
Keyword
breast cancer diagnosisimage pre-processingimaging modalitiesneural networks
DOI
10.3390/cancers13236116
URI
https://oak.ulsan.ac.kr/handle/2021.oak/9180
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_doaj_primary_oai_doaj_org_article_e64a8c5a924b4baca5cdef678c2ab063&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,A%20Comprehensive%20Survey%20on%20Deep-Learning-Based%20Breast%20Cancer%20Diagnosis&offset=0&pcAvailability=true
Publisher
Cancers
Location
스위스
Language
영어
ISSN
2072-6694
Citation Volume
13
Citation Number
23
Citation Start Page
6116
Citation End Page
6116
Appears in Collections:
Engineering > IT Convergence
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