Mammogram Classification using Fuzzy Neural Network

Breast cancer is one of the major causes for the increased mortality among women especially in developed countries. It is second most common cancer in women. The World Health Organization’s International estimated that more than 1, 50,000 women worldwide die of breast cancer in year. In India, breast cancer accounts for 23% of all the female cancer death followed by cervical cancer which accounts to 17.5% in India. Early detection of cancer leads to significant improvements in conservation treatment. However, recent studies have shown that the sensitivity of these systems is significantly decreased as the density of the breast increased while the specificity of the systems remained relatively constant. Mammography is a medical imaging technique that combines, low-dose radiation and high-contrast, high resolution film for examination of the breast and screening for breast cancer. Another disadvantage is false-positive result. This research proposes a fuzzy neural network for classifying mammograms. Results of screening the mammograms are organized by classification and finally grouped into three categories i.e., Normal, malignant and Benign. Experimental results show that this method performs well with the classification accuracy reaching nearly 82% in comparison with the already existing algorithms. The fuzzy neural network has provided high accuracy in the early diagnosis of Mammography, which can provide quantitative indicators for early clinical diagnosis and serve as a convenient diagnostic tool for physicians.