A Novel Brain MRI Image Classification Model using Density Featured Deep Super Learning (DFDSL) Classifier

Abstract

The brain image automation has improved a lot of features to analyse and predict the infected tissue region in enhanced classification accuracy. For that several methods were focused on the hybridization of algorithms to improve the quality of prediction. This paper proposed a novel model of image classification to predict the abnormality of brain image by using Density Featured Deep Super Learning (DFDSL) Classifier method. This classifies the image features for the combination of 3 set of MRI image types such as, T1-weighted, T2-weighted and FLAIR type of images for individual participants. This combination of image classification can be achieved by using the image registration process using Fractional Fourier Transform with spatial representation for the pre-processed image to identify the matching key points among the image set and merge it into one image data. In this, the image pre-processing was processed by using the Laplacian Cellular Automata Filtering (LCAF) to filter the noise present in the raw image. Then, from that the image can be classified by using the proposed DFDSL classifier. In the DFDSL classifier, it extracts the textural feature parameters of that image which is consider as for the feature learning of classifier. This can identify the clear depth in the image pixel variation which helps to improve the performance of image classifier. The result analysis and the comparison result shows the performance of proposed result compare to other state-of-art methods.