  Mass classification of objects is an important area of research and
application in a variety of fields. In this paper, we present an efficient
computer aided mass classification method in digitized mammograms using Fuzzy
K-Nearest Neighbor Equality, which performs benign or malignant classification
on region of interest that contains mass. One of the major mammographic
characteristics for mass classification is texture. Fuzzy K-Nearest Neighbor
Equality exploits this important factor to classify the mass into benign or
malignant. The statistical textural features used in characterizing the masses
are Haralick and Run length features. The main aim of the method is to increase
the effectiveness and efficiency of the classification process in an objective
manner to reduce the numbers of false positive of malignancies. In this paper
proposes a novel Fuzzy K-Nearest Neighbor Equality algorithm for classifying
the marked regions into benign and malignant and 94.46 sensitivity,96.81
specificity and 96.52 accuracy is achieved that is very much promising compare
to the radiologists' accuracy.
