  Breast cancer is considered as the most fatal type of cancer among women
worldwide and it is crucially important to be diagnosed at its early stages. In
the current study, we aim to represent a fast and efficient framework which
consists of two main parts:1- image classification, and 2- tumor region
segmentation. At the initial stage, the images are classified into the two
categories of normal and abnormal. Since the Deep Neural Networks have
performed successfully in machine vision task, we would employ the
convolutional neural networks for the classification of images. In the second
stage, the suggested framework is to diagnose and segment the tumor in the
mammography images. First, the mammography images are pre-processed by removing
noise and artifacts, and then, segment the image using the level-set algorithm
based on the spatial fuzzy c-means clustering. The proper initialization and
optimal configuration have strong effects on the performance of the level-set
segmentation. Thus, in our suggested framework, we have improved the level-set
algorithm by utilizing the spatial fuzzy c-means clustering which ultimately
results in a more precise segmentation. In order to evaluate the proposed
approach, we conducted experiments using the Mammographic Image Analysis (MIAS)
dataset. The tests have shown that the convolutional neural networks could
achieve high accuracy in classification of images. Moreover, the improved
level-set segmentation method, along with the fuzzy c-means clustering, could
perfectly do the segmentation on the tumor area. The suggested method has
classified the images with the accuracy of 78% and the AUC of 69%, which, as
compared to the previous methods, is 2% more accurate and 6% better AUC; and
has been able to extract the tumor area in a more precise way.
