Classification Of Micro Calsification And Categorization Of Breast Abnormalities – Benign And Malignant In Digital Mammograms Using Sne And Dwt

Mammography is used as a diagnostic and a screening tool that uses X-rays[1]. The objective of mammography is the premature revealing of breast cancer, usually through detection of characteristic masses and/or microcalcifications. Mammography is believed to decrease mortality from breast cancer[2]. This research work deals with classification of micro calcifications and mass in digital mammograms based on Discrete Wavelet Transform (DWT), Stochastic Neighbor Embedding (SNE) and the classifiers, K-Nearest Neighbor (KNN) and Support Vector Machine (SVM). Experimental results show that the proposed methods are successful in classifying the microcalcification in the digital mammogram.