On computer-aided diagnosis of breast cancers using nuclear atypia scoring

Abstract

Computer-aided systems are gaining interest in every field over the world. The medical field has also enhanced to a great extent with the use of computer-aided diagnosis. Among the different diseases in the era, breast cancer has shown a rapid hike in the number of deaths. Scoring of nuclear atypia is an efficient method for the prognosis of breast cancer. The biopsy samples taken from the suspicious tissues are analysed under microscope by the pathologists and are graded. But manual grading highly depends on the pathologists and can cause variation in the results. Hence, the requirement of computer-aided systems for grading has increased. Many studies related to nuclear atypia scoring have taken place in the literature based on different algorithms and classifiers. This paper gives an overview of the different studies in the literature, related to nuclear atypia scoring. Various techniques are used for nuclear atypia scoring. Multifarious image processing techniques are used for this. The aim of this study is to analyse these techniques and their results and know the most efficient one from them. Our analysis shows that promising results are achieved by machine learning techniques. Scores obtained using these techniques are comparable to manual grading.