Summary To recognize the breast cancer in the beginning stage, we make use of a technique called Mammography. By enforcing the different image Classification techniques like preprocessing, image enhancement, segmentation, morphological smoothening, features detection, feature extraction, and classification, we can enhance the accuracy and efficiency of the breast cancer detection. In every technique, different methods were utilized and major classifiers like KNN, J48, Naïve Bayes, and SVM classifiers, respectively. The acquired details were gathered and executed on a Weka tool to proceed further. The experimental analyses were done with the mammogram images, and those information's were enforced in the Weka tool as a training set. The Naïve Bayes classifier provides good results when distinguished with the current existing SVM classifier, and it is confirmed that Naïve Bayes works much better than SVM.

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