Optimized Algorithm for Lowering the Computation Time and Memory Utilization for Grading of Brain Cancers

Abstract

Clinical MRI scanning serves an essential part in the diagnostic procedure of several severe disorders including brain cancers and the following medication procedures of a patient. Because the brain is a fragile, intricate, and crucial part of the human body, it is one of the most common causes of death among cancer patients. However, a good and prompt treatment may save lives to a certain degree. Hence, in this publication, an effective brain tumor identification framework is suggested using a Deformable model of Fuzzy C-Mean clustering (DMFCM), Adaptive Cluster with Super Pixel Segmentation (ACSP), and Gray Wolf Optimization with Adaptive Clustering with Super pixel Segmentation (GWO_ACSP) and are mainly tested on CANCER IMAGE ACHRCHIEVE (CIA) which is a database containing High Grade and Low-Grade astrocytoma tumor images and also with BRATS 2015. The evaluation matrices were computed in which the proposed Gray Wolf Optimization-based ACSP (GWO_ACSP) gives a better answer for brain tumor segmentation with an accuracy of 0.99% than other models like RG, PFCM, SLPSO, MRG. The computational time is reduced to 80% and program memory utilization of about 300% is actually used in the proposed algorithms which shows a remarkable lower value compared to other prominent methods: