A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks

• URL: http://arxiv.org/abs/2003.12255v2
• Date: Thu, 18 Jun 2020 12:48:38 GMT
• Title: A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks
• Authors: Xiaomin Zhou, Chen Li, Md Mamunur Rahaman, Yudong Yao, Shiliang Ai, Changhao Sun, Xiaoyan Li, Qian Wang, Tao Jiang
• Abstract summary: Breast cancer is one of the most common and deadliest cancers among women. Artificial Neural Network (ANN) approaches are widely used in the segmentation and classification tasks. In this review, we present a comprehensive overview of the BHIA techniques based on ANNs.
• Score: 19.847428358596453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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