Breast Cancer Images Segmentation using Fuzzy Cellular Automaton
Iulia-Andreea Ion, Cristiana Moroz-Dubenco, Anca Andreica · Procedia Computer Science · 2023
Breast cancer is the most common type of cancer found in women. One of the most effiective methods for early identification of breast cancer is the mammogram. Numerous computer-aided systems for detecting breast cancer from mammograms have been introduced. In this paper, we present a new way for combining Cellular Automata with Fuzzy Logic, resulting in a so-called Fuzzy Cellular Automaton. The results obtained by testing our proposed approach on the mini-MIAS dataset are close to the ground truth, which is highly encouraging. The choice of using fuzzy logic provides a more flexible technique for categorizing the pixels of interest. The suggested method produces promising results for segmenting the mass region in mammograms with an accuracy of 98,66%, according to the experimental results.