Enhanced prediction of glioma brain tumors using deep learning algorithm

Immunotherapy has shown to be a viable strategy f or many malignancies in clinical trials, but its implementation in glioma has lagged behind the progress shown in other tumors. With IDH1 mutation, many characteristics over imaging were significantly different. In accordance with the Minimum Reduction and also with few selection operator, nonlobar location, a higher proportion of attractive tumours, multifocal/multicentric distribution, and inadequate delineation of non-enhancing margins were all autonomous predictors of an IDH1 wild type. Here an algorithm to detect the mutuation status and status for the co-deletion of brain tumor based on the MRI scans considering different radionics feature using VGG-16 and with Modified Fuzzy C Means algorithm together with Grey Wolf Optimization in R CNN techniques. This innovative model aims to enhance the precision of diagnostic outcomes by employing error-free algorithms in the analysis for the examinations of medical image data.