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

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 Neighbour Equality (FK-NNE), which performs benign or malignant classification on region of interest that contains mass. One of the major mammographic characteristics for mass classification is texture. FKNNE 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 falsepositive of malignancies. In this paper proposes a novel Fuzzy K-Nearest Neighbour 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 radiologist’s accuracy.