Mammography diagnostic using recognition system based on closed loop fuzzy logic controller

Abstract

Breast cancer is the one of the most common invasive cancer type among women. Early detection and diagnosis of breast cancer can be facilitating the chance of better treatment for the cancer affected people with mammography image analysis, since mammograms are cost effective and the world standard for screening of breast. Extracting the features from mammograms and classifying the breast abnormalities are extremely important to help cancer detection and prevention.
There are many ways to classify the mammograms; in this paper we have proposed a closed loop adaptive FUZZY control system; for cancer recognition;
The recognition and classification systems are generally used for security, medical, real time and for decision making systems, so performing those recognition and classification systems and optimizing existing results are always sought. In literature, several holistic, hybrid and feature-based methods of recognition and classification are widely found using fuzzy, neural networks, support vector machine ….
This paper presents a new approach for cancer recognition based on closed loop adaptive FUZZY control system with an automatic error regulation; in essence, this strategy performs the cancer recognition.