An Effective Two Way Classification of Breast Cancer Images: A Detailed Review

Document Type : Research Articles


Department of EIE, Saveetha Engineering College, Chennai, India.


Cancer, a disease of cells, causes cell growth which differs from normal cell growth ratio, this cell growth spreads
in the human body and kills the body cells. Breast cancer, it’s a highly heterogeneous disease and western women
commonly witness this. Mammography, a pre-screening X-ray based check is used to diagnose woman’s breast cancer.
This basic test mode helps in identifying breast cancer at early stage and this early stage detection would support in
recovering more number of women from this serious disease. Medical centres deputed highly skilled radiologists and
they were given the responsibility of analysing this mammography results but still human errors are inevitable. An error
frequency ratio is high when radiologists exhausted in their analysis task and leads variations in either observations
ie., internal or external observation. Also, quality of the image plays vital role in Mammographic sensitivity and leads
to variation. Several automation processes were tried in streamlining and standardising diagnosis analysis process and
quality of breast cancer images were improved. This paper inducts a two way mode algorithm for grouping of breast
cancer images to 1. benign (tumour growing, but not dangerous) and 2. malignant (cannot be controlled, it causes death)
classes. Two-way mode data mining algorithms are used due to thinly dispersed distribution of abnormal mammograms.
First type algorithm is k-means algorithm, which regroups the given data elements into clusters (ie., prioritized by the
users). Second type algorithm is Support Vector Machine (SVM), which is used to identify the most suitable function
which differentiates the members based on the training data.


Main Subjects

Volume 19, Issue 12
December 2018
Pages 3335-3339
  • Receive Date: 01 March 2018
  • Revise Date: 29 July 2018
  • Accept Date: 27 October 2018
  • First Publish Date: 01 December 2018