Article
Mammogram Based on Breast Cancer Mining
With breast cancer still among leading causes of death in women all over the world, it is imperative that an early detection will make treatment possible. To enhance the rate of accuracy in diagnosis, this research is inclined to breast cancer mining through this approach in mammogram and by data mining and machine learning methods. The mammograms that have been processed and examined are sought to identify the patterns and characteristics that denote malignant tumors. To extract the features, texture analysis and shape descriptors are applied, whereas Support Vector Machines (SVM) and Convolutional Neural Networks (CNN) are applied as classification methods. The proposed approach aims to augment early detection as well as reduce the number of false positives. The evidence indicates that data-driven mammography analysis stands to be of great benefit to radiologists so they could make quicker and more accurate diagnoses of breast cancer in an effort to better the patient.
Full Text Attachment
