Cancer cell detection using advanced fuzzy set theories

Abstract

The past three decades, breast cancer has evolved rapidly to diagnosis and treatment for organizing breast screening and progress of imaging modalities. The programming languages like artificial intelligence helps for medical treatments and it is reduced the work of human guided. Breast cancer has develop into the second leading cause of death in women, improve the cancer patients safety through early diagnosis. Digital pathology plays an important role to detect the stages of cancer cell and it is help to improve the diagnosis accuracy. The proposed work of triangular intuitionistic fuzzy number based contrast limited adaptive histogram equalization was produced better results and handles the uncertainty in the medical images. It was implemented for select the clip limit value by automatically and get better image quality. The existing and proposed method is compared by image quality measurement such as mean square error and peak signal noise ratio.