Study on different classification technique for mammogram image

Breast cancer is one of the crucially prevailing cancer among women. Early detection and diagnosis of breast cancer can be facilitating with mammography images since they are most cost effective and a good chances of recovery. Classification is an identification technique used to organize the data into categories. Classification algorithm identifies the severity of lymph´s present in the breast. The entire study focuses on different classifier techniques which can be used after pre-processing and segmentation process to improve the accuracy result of the image and can be categorized as well. We made a study on suitable techniques for mammogram images such as decision tree, K-nearest Neighbour, Fuzzy K-Nearest Neighbor, Nave Bayes, Artificial Neural Network, Ensemble and Support vector Machine. For each classification, we consider the factor such as sensitivity, specificity and accuracy which are chosen according to their suitable scenarios.