TY - JOUR
T1 - A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
AU - Zhou, Xiaomin
AU - Li, Chen
AU - Rahaman, Md Mamunur
AU - Yao, Yudong
AU - Ai, Shiliang
AU - Sun, Changhao
AU - Wang, Qian
AU - Zhang, Yong
AU - Li, Mo
AU - Li, Xiaoyan
AU - Jiang, Tao
AU - Xue, Dan
AU - Qi, Shouliang
AU - Teng, Yueyang
N1 - Publisher Copyright:
© 2013 IEEE.
PY - 2020
Y1 - 2020
N2 - Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.
AB - Breast cancer is one of the most common and deadliest cancers among women. Since histopathological images contain sufficient phenotypic information, they play an indispensable role in the diagnosis and treatment of breast cancers. To improve the accuracy and objectivity of Breast Histopathological Image Analysis (BHIA), Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks of breast histopathological images. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs. First of all, we categorize the BHIA systems into classical and deep neural networks for in-depth investigation. Then, the relevant studies based on BHIA systems are presented. After that, we analyze the existing models to discover the most suitable algorithms. Finally, publicly accessible datasets, along with their download links, are provided for the convenience of future researchers.
KW - Breast cancer
KW - convolutional neural networks
KW - deep learning
KW - histopathology
KW - image classification
KW - image segmentation
UR - http://www.scopus.com/inward/record.url?scp=85085547747&partnerID=8YFLogxK
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U2 - 10.1109/ACCESS.2020.2993788
DO - 10.1109/ACCESS.2020.2993788
M3 - Article
AN - SCOPUS:85085547747
VL - 8
SP - 90931
EP - 90956
JO - IEEE Access
JF - IEEE Access
M1 - 9091012
ER -