An Efficient Method for Automatic Classification of Brain MRI using Feature Selection and Modified Probabilistic Neural Network

In the brain tumor MRI images, the identification, segmentation and detection of the infectious area is a tedious and lengthy task. As segmentation is called intensity inhomogeneity by an intrinsic object. In this paper we suggest an energy efficient minimization technique for joint domain assessment and segmentation of MR images called multiplicative intrinsic component optimization (MICO). In this work, we focused on quicker implementation with a robust removal of gray-level co-occurrence matrix (GLCM). Optimal texture characteristics are obtained by the Spatial Gray Dependence (SGLDM) technique from ordinary and tumor areas. With very large feature sets, the choice of features is redundant because the precision frequently worsens without choice of features. However, when only the feature selection is used, the precision of classification is significantly improved. However, by reducing the time needed for classification computations and improving classification precision by removing redundant, false or incorrect characteristics. A fresh function choice and weighting technique, supported by the decomposition developmental multi-objective algorithm, are provided in this work. These characteristics are provided for the MPNN classification. Modified probabilistic neural network (MPNN) classification was used in brain MRI images for training and testing for precision in tumor identification. The simulation findings accomplished almost 98% precision in the identification of ordinary and abnormal tissue from brain MR images showing the efficiency of the method suggested.