Breast cancer classification using improved fuzzy c means algorithm

Hye-jin Kim

Kookmin University, Jeongneung-ro, Seongbuk-gu, Seoul, Korea

ABSTRACT

Abnormal growth in the breast tissue prompts to the strange cell development in the breast. The researchers typically research for the size of the tumour in a mammogram, because mammograms contain irregular measurements of large scale and smaller scale calcifications. The nearness of these irregular measures of calcium stores in the breast ought to never be ignored as these are indications of early breast malignancy. To decipher this statement in a mammogram precisely, the quality of the pictures ought to be at its incomparable. The proposed research work is conveyed out for examinations of different screening strategies to recognize the unique phases of breast malignancy. In India for every 4 minutes, the women are diagnosed with this disease. And a woman dies with this disease for every 13 minutes. This disease is prominent with the people living in the ruler area while comparing the people in the urban areas. Therefore, it is very important to find and treat this disease as early as possible. The breast tumour region, perimeter and breadth are assessed from mammogram picture databases. The Bits Errors Degree (BER),), Highest Indication to Clatter Percentage (PSNR) and Callous Tetragonal Inaccuracy (MSE) values are determined for both Abnormal and normal images. These analyses were used to approve the presence or absence of the disease and to support the evaluation process for finding the disease. This quality assessment is used to understand the reality on Earth for a specific diagnosis that is a specific type of chromatin in a carcinogenic core that may indicate an irregular protein sequence.

Keywords:

PSNR, MSE, Malignancy, Cancer, Classification, Fuzzy Means Algorithm.