Classification of Breast Cancer Tumors Using Mammography Images Processing Based on Machine Learning


 Abstract— Using intelligent methods to identify and classify a variety of diseases, in particular cancer, has gained tremendous attention today. Tumor classification plays an important role medical diagnosis. This study's goal was breast cancer (BC) tumors using software-based numerical techniques. To determine whether masses are benign or malignant, we used MATLAB version 2020b build neural network. In the first step, features training images their output classes were train Optimal weights obtained after several repetitions, network trained produce best result test phase repetitions.
 Because effective accurate features, method suggested here, which based on artificial network, delivered diagnostic accuracy, specificity, sensitivity 100%, respectively, discern from malignant BC tumors, showing better performance compared previously proposed methods. One challenges for imaging-based techniques medicine is difficulty processing dense tissues. Breast one most common progressive diseases among females. Early diagnosis makes treatment easier more effective. AI-based automated purposes can be valuable have reduced error rate because by manual means time-consuming error-prone.