Comparative Study on Cancer Image Diagnosis using Soft Computing Techniques

Abstract

Computer-aided diagnosis system (CAD) can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs. CAD as such employs several techniques to accomplish this task. In this paper, we propose to make a comparative study of two classification methods: One in which we utilize the texture features extracted from the images by directly feeding to the Neural Network based classifier stage to classify the images into benign or malign and in the other hybrid method, those texture features are made to undergo fuzzy discretization before feeding to the Neural Network classifier for the classification. The studies so far conducted using both the systems show that the hybrid system is far superior to the first method in its accuracy. Backward Propagation Network (BPN) algorithm is used in the training stage.

A study on mammography computer aided diagnosis system using machine learning methods

“…In the literature there are several methods employed in discretizing the continuous values. Among them Fuzzy Discretization [14] proved to be very fast method. Fuzzy partition method overcomes the limitation such as uncertainty, loss of information at the boundaries.…”