Wang Y, Shi H, Ma S
Room 631, Mathematics Building, Jilin University, 2699 Qianjin Street, Changchun 130012, China.
J Int Med Res. 2011;39(6):2256-63. doi: 10.1177/147323001103900622.
Breast cancer is a leading cause of female mortality and its early detection is an important means of reducing this. The present study investigated an approach, based on fuzzy clustering, to detect small lesions, such as microcalcifications and other masses, that are hard to recognize in breast cancer screening. A total of 180 mammograms were analysed and classified by radiologists into three groups (n = 60 per group): those with microcalcifications; those with tumours; and those with no lesions. Twenty mammograms were taken as training data sets from each of the groups. The algorithm was then applied to the data not taken for training. Analysis by fuzzy clustering achieved a mean accuracy of 99.7% compared with the radiologists' findings. It was concluded that the fuzzy clustering algorithm allowed for more efficient and accurate detection of breast lesions and may improve the early detection of breast tumours.
