BREAST CANCER DETECTION USING COMPUTATIONAL INTELLIGENCE

Mammograms are the best tool to detect an early disease of breast cancer. In
mammography, medical experts look for clustered microcalcifications and
irregular density masses. As microcalcification is a tiny speck of calcium in
breast, it appears as white spot in mammogram. Problem occurred when the
clinician reads the mammograms using a magnifying glass, as it is difficult to
detect calcification because there is a wide range of abnormalities and it also due
to the small size and their similarity with other tissue structure. One of the
problems is to distinguish between malignant and benign tumors. Thus, the
objectives of this project are to enhance mammogram image using image
processing technique and to provide a pattern recognition system by signifying
whether further investigation is needed, therefore it may assist medical expert in
detection of breast cancer. Accordingly, the scope of this project is based on the
pattern recognition system, which includes preprocessing, feature extraction, and
classification. The task for the project is divided into two parts. The first part is
the enhancement of the image and the detection of calcification. The second part
of the project is to design, develop, and test the network whether it run as
expected. As the result, mammogram images have been processed through image
processing by using MATLAB, and opening morphological operation has been
used for the detection. A pattern recognition system has been developed by the
use of neural network. As a conclusion, a successful implementation of pattern
recognition system as one way to detect breast cancer could help medical field in
diagnosing breast cancer.