Abstract
AbstractBreast cancer is one of the most widely recognized diseases among women across the globe. It is cancer that develops in breast cells. Histopathological and cytological images contain adequate phenotypic data, which defines their vital role in the analysis and cure of breast cancer. The emergence of deep neural networks with the rapid advancement in the Computational resources automatically inclines the accurate detection and classification of the Breast Histopathological Images. It assists the histopathologists to achieve accurate results through progressively fast, steady, objective, and measured examination. In this paper, a systematic survey has been conducted to introduce the improvement history and locate the future competence of Deep Learning calculations in the Breast Histopathological Image Analysis (BHIA) field. This study also involves comparative analysis of the most recent related works alluding to classical Artificial Neural Networks (ANNs) and Deep ANNs.KeywordsBreast cancerClassificationHistopathology imageArtificial neural networksConvolutional neural networksDeep learningFeature extraction
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