BREAST CANCER DETECTION USING COMPUTATIONAL INTELLIGENCE - FADILLULLAH, SITI AISHAH (Universiti Teknologi Petronas, 2005)
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.