Certain investigations on efficient detection and classification of breast cancer in digital mammogram using transform techniques and machine learning

Abstract

Cancer is a group of diseases that leads cells within the body to change and go out of control. Breast cancer is a major reason for death among both young and old women. The National Cancer Institute of United State calculates that one out of eight women would from breast cancer at some point during her lifetime. Early detection is the key to improve the breast cancer prognosis. It is well known that early detection of cancer could assist in good recovery and prolong patient s life. Due to this reason, radiologists need to identify breast cancer at an initial stage. X-ray mammography is the most common method available to radiologists in screening and diagnosis of breast cancer. Digital mammography has been utilized in order to maximize the negative biopsy. This itself poses a unique problem because there is a lot of inter- observer differences which prevail while diagnosing breast cancer through mammograms. The objective of the Computer Aided Diagnosis (CAD) in radiology is to enhance the diagnostic aptness as well as the consistency of radiologist s image interpretation by making use of computer output as a guide. The early detection and accurate diagnosis of breast cancer is still an unresolved challenge in modern computer aided detection and analysis. Though biopsies are taken, tumors often go untraced until a period, where therapy is costly or unsuccessful. Forming a computer assisted diagnosis device for cancer diseases, like breast cancer, to help physicians in hospitals is becoming highly significant and priority for many analysts and clinical centers. It is a complex process to form a computer vision system to execute such tasks. Various methods have been employed and elaborated in literature to accomplish this task. newline