Author Description

Breast cancer is horrendous disease after skin cancer which is most common in woman and it is a foremost cause for the upsurge in mortality rate. Screening mammography is the operative procedure for detecting masses and abnormalities allied to breast cancer. Digital mammograms are utmost operative source that helps in early detection of cancer in women with no symptoms and diagnose cancer in women with symptoms like pain in lump, nipple discharge which diminutions deaths and upsurges chances of survival. Usually clinician cannot spare more time on a patient to weigh the complaints and suggest a possible diagnosis by considering past records.During this stage, there is more chance to medical errors and wrong diagnosis. By using machine learning in diagnosing breast cancer improves accuracy by reducing misclassifications and saves time in diagnosing. The proposed work is instinctive classification of mammogram images as Benign, Malignant and Normal using various machine learning algorithms. Classification is an identification technique used to classify consolidated data into different categories.