Hybrid optimized framework for classification of breast cancer

Abstract

Breast cancer is a common cancer among women. Though potentially fatal early diagnosis can result in successful treatment An important step in breast cancer diagnosis is tumor classification Tumors are either benign or malignant and only the latter is cancer The diagnosis requires precise and reliable diagnosis to ensure that doctors can distinguish between benign and malignant tumors Mammography is presently an effective imaging modality for breast cancer abnormalities detection Extracting features refers to the simplification of the quantity of vectors that are needed for describing big data sets in an accurate manner. Selecting features is also significant in detecting breast cancers and subsequently classifying them Computer Aided Detection CAD systems generally perform automatic assessments of patient images and present to radiologists areas that they have determined as having the appearance of an abnormality. It is important to have awareness that in different contexts CAD can have different performances so it needs to be adjusted to produce the most accurate result It is clearly seen that CAD systems are very useful for health professionals newline