Brain Tumor Detection using Cellular Automata based image Segmentation techniques - Elfatimi, Lahcen; Boucheneb, Hanifa (International Journal of Scientific Research in Computer Science and Engineering, 2022)
A quick and effective diagnosis is critical to the treatment of any disease. In the case of cancer-related diseases, the success of the treatment is typically correlated to the time of accurate diagnosis of the disease. Thus, it is important to make a quick and accurate diagnosis. This work presents a cellular automata-based system capable of diagnosing tumors in medical data from various imaging techniques, including MRI and X-ray. This system is an Automated Cellular (CA) that uses the Moore neighborhood algorithm to detect the area of cancer cells in the image, by segmenting areas of abnormality from the background. We also present an analysis of different parameters of the Moore neighborhood algorithm for optimal detection of cancerous cells; the results of this analysis confirm the proposed method effectiveness on all data sets, with an accuracy of more than 93% and 95% precision.