Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural

TLDR: Two techniques are proposed based on wavelet analysis and fuzzy-neural approaches that can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs.

Abstract: The high incidence of breast cancer in women has increased significantly in the recent years. The most familiar breast tumors types are mass and microcalcification. Mammograms-breast X-ray-are considered the most reliable method in early detection of breast cancer. Computer-aided diagnosis system can be very helpful for radiologist in detection and diagnosing abnormalities earlier and faster than traditional screening programs. Several techniques can be used to accomplish this task. In this paper, two techniques are proposed based on wavelet analysis and fuzzy-neural approaches. These techniques are mammography classifier based on globally processed image and mammography classifier based on locally processed image (region of interest). The system is classified normal from abnormal, mass for microcalcification and abnormal severity (benign or malignant). The evaluation of the system is carried out on Mammography Image Analysis Society (MIAS) dataset. The accuracy achieved is satisfied.