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.
Highlights
Breast cancer is the most commonly diagnosed and leading cause of cancer deaths among women [1]
MOTIVATION OF OUR REVIEW PAPER This paper focuses on the work of Artificial Neural Network (ANN) in the image analysis of breast histopathology
In our previous work [33], we propose a brief survey for breast histopathology image analysis using classical and deep neural networks
Summary
Breast cancer is the most commonly diagnosed and leading cause of cancer deaths among women [1]. More than 130 papers about histopathological image analysis are summarized, but only five are about BHIA with ANNs. The survey of [12] publishes a research survey, focusing on the use of AI and deep learning in the diagnosis of breast pathology images, and other recent developments in digital image analysis. In [32], an overview of ‘‘recent trends in computer assisted diagnosis system for breast cancer diagnosis using histopathological images’’ with 106 related works is presented This review summarizes those works by four technical steps, including image pre-processing, segmentation, feature extraction and selection, as well as classification. In our previous work [33], we propose a brief survey for breast histopathology image analysis using classical and deep neural networks.
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