Paper Details

Microcalcifications are one of the key symptoms facilitating early detection of breast cancer. In this paper,textural features are extracted from the segmented mammogram image to classify the microcalcifications into benign,malignant or normal. The tumor part of the breast region is extracted by the reduction algorithm. Initially the reducedfeatures are normalized between zero and one. The normalized feature values are given as input to a three-layer BPN toclassify the microcalcifications into benign, malignant or normal. The network is trained to produce the output value fornormal images. In this research proposed an image extraction method with reduced computation time was proposed. Thecomputational time of proposed approach is obtained by use of Particle Swarm Optimization (PSO) algorithm. Themembership function of this conventional algorithm is sensitive to the outline and does not integrate the spatialinformation in the image. To resolve existing disadvantages in classification algorithms like sensitivity, noise andin-homogeneties are overcome in proposed approach. Existing drawbacks are overcome in our algorithm takes also intoconsideration the spatial neighborhood information. To resolve existing limitation in this research proposed a weightedparticle swarm optimization technique for cancer extraction. Based on the weighted function in optimization algorithmimages are clustered and extracted. Simulation results of the proposed approach are comparatively examined withexisting approaches naive bayes, Adaboost and SVM. Comparison of results demonstrated that proposed approachexhibits effective performance than existing classification algorithm.

R. Seetha, S. Santhosh baboo, “A Weighted Particle Swarm Optimization (WPSO) technique for accurate breast cancer detection”, International Journal of Advance Engineering and Research Development (IJAERD), Vol. 4, Issue 10, pp. 454-463, October 2017.