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Mass classification of objects is an important area of research andapplication in a variety of fields. In this paper, we present an efficientcomputer aided mass classification method in digitized mammograms using FuzzyK-Nearest Neighbor Equality, which performs benign or malignant classificationon region of interest that contains mass. One of the major mammographiccharacteristics for mass classification is texture. Fuzzy K-Nearest NeighborEquality exploits this important factor to classify the mass into benign ormalignant. The statistical textural features used in characterizing the massesare Haralick and Run length features. The main aim of the method is to increasethe effectiveness and efficiency of the classification process in an objectivemanner to reduce the numbers of false positive of malignancies. In this paperproposes a novel Fuzzy K-Nearest Neighbor Equality algorithm for classifyingthe marked regions into benign and malignant and 94.46 sensitivity,96.81specificity and 96.52 accuracy is achieved that is very much promising compareto the radiologists' accuracy.

Mass Classification Method in Mammogram Using Fuzzy K-Nearest Neighbour Equality