Mammogram Preprocessing Techniques and Mammogram Classification

Abstract

Breast cancer is a disease in which cells in the breast grow out of control. There are different kinds of breast cancer. The kind of breast cancer depends on which cells in the breast turn into cancer. Breast cancer can begin in different parts of the breast. A breast is made up of three main parts: lobules, ducts, and connective tissue. The lobules are the glands that produce milk. The ducts are tubes that carry milk to the nipple. The connective tissue (which consists of fibrous and fatty tissue) surrounds and holds everything together. Most breast cancers begin in the ducts or lobules. Breast cancer can spread outside the breast through blood vessels and lymph vessels. When breast cancer spreads to other parts of the body, it is said to have metastasized. The goal of this study is to offer best preprocessing technique for the identification of breast cancer by critically analysing the literature currently available in this field. Numerous studies with related objectives have been described in the research literature. In this study, there is comparison highlighting the methods used for preliminary image processing and classification for mammography images. In addition, we have created an Ensemble Learner based on the preprocessed image dataset and the performance assessment is done based on the factors for the scrutiny of outcomes for classification of breast cancer into the types: Benign or Malignant. The first step is to select the best preprocessing technique among 10 preprocessing techniques. Then 5 Filters have been applied on it for decreasing the Mean Square Error and then the Mini DDSM Dataset is to be segmented using Region based segmentation. At last there is an ensemble learner created. There are 4 DL Models used for the processing and then XG Boost Classifier has been used for taking the final output of the Ensemble Learner for the classification of Breast cancer into the types Benign and Malignant.