模式
计算机科学
乳腺癌
背景(考古学)
人工智能
深度学习
机器学习
医学影像学
癌症检测
模态(人机交互)
乳房成像
医学物理学
乳腺摄影术
癌症
医学
内科学
社会学
古生物学
生物
社会科学
作者
Essam H. Houssein,Marwa M. Emam,Abdelmgeid A. Ali,Ponnuthurai Nagaratnam Suganthan
标识
DOI:10.1016/j.eswa.2020.114161
摘要
Breast cancer is the second leading cause of death for women, so accurate early detection can help decrease breast cancer mortality rates. Computer-aided detection allows radiologists to detect abnormalities efficiently. Medical images are sources of information relevant to the detection and diagnosis of various diseases and abnormalities. Several modalities allow radiologists to study the internal structure, and these modalities have been met with great interest in several types of research. In some medical fields, each of these modalities is of considerable significance. This study aims at presenting a review that shows the new applications of machine learning and deep learning technology for detecting and classifying breast cancer and provides an overview of progress in this area. This review reflects on the classification of breast cancer utilizing multi-modalities medical imaging. Details are also given on techniques developed to facilitate the classification of tumors, non-tumors, and dense masses in various medical imaging modalities. It first provides an overview of the different approaches to machine learning, then an overview of the different deep learning techniques and specific architectures for the detection and classification of breast cancer. We also provide a brief overview of the different image modalities to give a complete overview of the area. In the same context, this review was performed using a broad variety of research databases as a source of information for access to various field publications. Finally, this review summarizes the future trends and challenges in the classification and detection of breast cancer.
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