A review of automated digital clinical system of breast cancer detection using fine needle aspiration cytology images

Screening of microscopic slides is a manual process that involves its subjectivity. A semi-automated computer-based system can contribute to the detection of screening errors by increasing the reliability measure. Traditional machine learning approach or deep learning approach can be used in the semi-automated digital clinical system. The traditional machine learning approach is not very efficient because it involves a lot of heavy mathematics and not able to learn highly complex features. This article presents a systematic summary of the existing solutions of detection of malignancy (breast cancer detection) from fine-needle aspiration cytology images and the segmentation method of nuclei because malignancy can be observed mainly from nuclei feature. It also reports various research issues, challenges and proposes the future research direction. This analysis is helpful for the better use of existing methods and for improving their performance, as well as designing new methods and techniques.