Breast Cancer Detection, Segmentation and Classification on Histopathology Images Analysis: A Systematic Review

Abstract

Digital pathology represents a major evolution in modern medicine. Pathological examinations constitute the standard in medical protocols and the law, and call for specific action in the diagnostic process. Advances in digital pathology have made it possible for image analysis to take advantage of the information analysis from hematoxylin and eosin stained images. In spite of concern, it is recorded in the majority of breast cancer datasets, which makes research more difficult in prediction. The objective of our work is to evaluate the performance of the machine learning and deep learning techniques applied to predict breast cancer recurrence rates. This study starts with an overview of tissue preparation, analysis of stained images, and a prognosis for cancer patients. The high accuracy results recorded are compromised in terms of sensitivity and specificity. The missing loss function and class imbalance problems are rarely addressed, and most often the chosen performance measures are context-inappropriate. The challenge that presents itself is to analyse whole slide images for the content imaging required with diagnostic biomarkers, and prognosis support backed by digital pathology.