Breast Cancer Diagnosis Based on Frequency Converters and Extraction of Effective Features

Breast cancer is the most common type of cancer among women. Early diagnosis of this disease and its treatment can significantly reduce the death rate from this cancer. The separation of benign and malignant masses in mammography images is one of the important things in the timely detection of breast cancer, which in some cases, due to the density and natural structure of the breast, deep and hidden disorders, make the diagnosis difficult for radiologists. In this study, frequency transformations and Naive Bayes classification have been used with the aim of extracting effective features in mammography images. The aim of the presented method is to increase the accuracy of diagnosis between malignant and benign tumors in mammography images. The results obtained from the implementation of the proposed method on the MIAS database show that the proposed method has been able to improve the accuracy of diagnosing this disease on normal and abnormal images by 91%, Precision by 98%, Recall by 987%, and F-measure by 90%.

Breast Cancer Diagnosis Based on Frequency Converters and Extraction of Effective Features Keywords:

breast cancer , Frequency Converters , Mammography Images , Feature Extraction , Classification