IDENTIFICATION OF TUMOUR USING K-MEANS ALGORITHM

ELECTRONICS AND COMMUNICATION ENGINEERING

Breast cancer is cancer that develops from breast tissue. Signs of breast cancer may include a lump in the breast, a change in breast shape, dimpling of the skin, fluid coming from the nipple, or a red scaly patch of skin. In those with distant spread of the disease, there may be bone pain, swollen lymph nodes, shortness of breath, or yellow skin. The uncontrolled division of cells is termed as cancer. It is a highly heterogeneous disease and western women commonly witness this. Mammography is used to diagnose breast cancer. Sometimes the mammography results and additional tests may be performed in special circumstances.This basic test mode helps in identifying breast cancer at early stage and this early stage detection would support in recovering more number of women from this serious disease. Medical centres depute highly skilled radiologists & were given responsibility of analysing this mammography results but still human errors are inevitable. An error frequency ratio is high when radiologists exhausted in their analysis task and leads variations in either observations i.e., internal or external observation. Also, quality of the image plays vital role in Mammographic sensitivity and leads variation. In this research work, the automation process using k-means clustering algorithm is attempted in diagnosis of breast cancer. The algorithm yielded a quality analysis process for breast cancer images.

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