Tumor Detection with Iterative Cellular Automata

Early diagnosis is critical in cancer related diseases as necessary interventions can only be set in place once an accurate diagnosis is conducted. Cellular Automata (CA) based frameworks allow for a robust isolation of tumor regions in medical images. In this work, a Cellular Automata based iterative approach is presented that can isolate tumors effectively. This system utilizes a 24 -point Moore neighborhood configuration to identify areas of cancerous cells in an image by segmenting areas of abnormality from the background. The iterative CA based approach results in 96% accuracy and 98% precision. The proposed methodology will aid physicians in effective cancer diagnosis.