乳腺癌
模式
计算机科学
鉴定(生物学)
人工智能
深度学习
任务(项目管理)
机器学习
医疗保健
疾病
领域(数学分析)
癌症
医学
病理
内科学
社会学
数学分析
数学
经济
管理
生物
植物
经济增长
社会科学
作者
C. Sarada,V. Dattatreya,K. Vijaya Lakshmi
标识
DOI:10.1109/icicacs57338.2023.10100206
摘要
Many human beings are losing life yearly due to Breast Cancer. Breast Cancer detection is a challenging task where skilled radiologists are essential to detect it. The manual identification of Breast Cancer illness involves a significant amount of time, and the manual treatment of disease also demands a significant amount of time. So automated detection is needed, to help in giving early treatment and, in some cases, prevents life risk. Recently, many advances have been made in the health care domain. Because of resource availability and computation capacity, these technological improvements are helpful for early treatment. This survey paper covers all the modern approaches applied on various datasets, which helps the researchers to improve the outcomes in the effective identification of Breast Cancer. This is a review study that examines approximately 30 deep learning-based classification mechanisms for Breast Cancer detection with different types of modalities.
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