Analysis of breast cancer cell based on hyperchromatic crowded group using multiple techniques of data mining
Chayanika Sarmah · Computer Science and Software Engineering · 2018
In this paper, an effort is made to analyze MGG stain images of breast cell in FNAC which will help in early detection of malignant breast cancer cell. There are many morphological features based on which MGG stain test smear images can be categorized into normal and abnormal classes. Some of them are area, perimeter and presence of hyperchromatic crowded (HCG) group etc. So, in this approach we analyse the breast cell based on a presence in the malignant cell using multiple techniques of data mining (Images are examined). The proposed approach is implemented in WEKA, a java based data mining tool KEYWORDS: breast cancer, HCG, MGG, FNAC.