Diagnosis of breast cancer using K-Means and fuzzy C-Means clustering

In recent years, breast cancer has been one of the most common causes of death among women.Thermography is one of the fastest, cheapest, risk-free, radiation-free, and painless diagnostic methodsavailable for this cancer. The use of new methods in image processing and machine learning has led to theuse of thermographic images to successfully conduct studies to establish breast cancer diagnostic systems. Inthis study, the diagnosis of breast cancer has been studied using K-Mean and fuzzy C-Mean clustering andan intelligent method has been used to separate healthy from unhealthy tissue and to separate mass inunhealthy tissue. In this method, clustering was performed using two methods: K-Mean and fuzzy C-Mean,then the cluster with the highest center intensity as the input of the area growth algorithm and the brightestpixel as the grain point of the area growth method were selected. The suspected area was determinedaccording to the growth algorithm of the area and then the specificity of the suspected area was extractedbased on the coefficient matrix. At this stage, the threshold was set for the four properties of the co-occurrencematrix and based on it, a decision was made about the suspicious area. Using this method, an intelligentsystem was designed that reduces the amount of human error in the diagnosis of cancer and will be able todetect the mass in the early stages of breast cancer with ۹۱.۶۷% classification and ۸۹.۶۵% sensitivity.

کلیدواژه ها:

Breast cancer ، Image processing ، K-Means clustering Method ، Fuzzy C- Means clustering Method