Detection of Brain Tumor with Cellular Automata and Convolutional Neural Networks Murugan A1, Harsha R2 1Associate Professor, Department of ECE, M. Kumarasamy College of Engineering, Karur, Tamil Nadu 2PG Scholar, Department of ECE, M. Kumarasamy College of Engineering, Karur, Tamil Nadu Online published on 15 March, 2019. Abstract High grade brain tumor are very aggressive and most common disease globally. As a result the life expectancy of the diseased is very short with respect to low grade tumor. Fitting for the treatment within the planning staggering stage is that the key plan for saving the lifetime of medicine patients. CT (CT) and resonance imaging (MRI) is that the typically used imaging techniques to contemplate the tumors, however the massive amount of knowledge fashioned by magnetic resonance imaging prevents the physical segmentation in Associate in Nursing emergency time, limiting the employment of correct quantitative measurements within the medical exercise. Regular detection and segmentation ways area unit needed, however massive spatial and structural variability among brain tumour makes automatic segmentation and detection is that the rigorous drawback. during this paper, we have a tendency to projected Associate in Nursing regular segmentation and detection technique supported Convolutional Neural Networks (CNN) and Cellular automata (CA) supported Gary-level co-occurrence matrix (GLCM) to work out native transition perform. We have a tendency to conjointly analysed the employment of C-NN based segmentation along with pre-processing step, which, though not commonly used on CNN-based segmentation ways. Our proposal was valid by the info taken from the hospitals. Finally, a neoplasm has been extracted from the given info image with the improved performance measures. Top Keywords Brain Tumour Segmentation, Convolutional Neural Networks (CNN), GLCM, Cellular Automata (CA), Deep Learning, Resonance Imaging (MRI). Top |