A modern approach to the diagnosis of breast cancer in women based on using Cellular Automata

Abstract

Breast cancer is one of the most common conditions as well as one of the most important factors of death among women. If diagnosed correctly and in time, it may cause fewer death tolls. The most important method of diagnosing this type of cancer is using mammography imaging. In some cases, the diagnosis may be incorrect. In the present article, Cellular Automata is used for the diagnosis of breast cancer type micro-calsification (or tiny calcium particle sediments). In this approach the mammography image of the alleged patient is converted to an optimum image through preprocessing. Next, this image is entered into a cellular network to determine its cluster center. The results achieved by this approach on DDSM indicated that diagnosis based on the approach introduced in this article can be a useful combination for the traditional methods.