Real-time Multi Fractal Ensemble Analysis CNN Model for Optimizing Brain Tumor Classification and Survival Prediction Using SVM
M. Vimala1
and P. Ranjith Kumar2 Department of ECE, P.S.R Engineering College, Sivakasi, Tamil Nadu- 626 140, India.
Corresponding Author E-mail: vimala@psr.edu.in
Abstract: Classification and Prediction of brain tumors towards survival prediction has been well studied. There exist different schemes around the problem but struggle with poor performance in survival prediction and classification. To overcome the deficiency in classification, a real-time multi-fractal ensemble analysis CNN model (RMFEA-CNN) is presented in this article. The method not just considers basic low-level features like gray, texture, and binary features but also considers Coverage, Mass Index, and Intensity Fraction features. By preprocessing the image with the histogram equalization technique, the image quality has been increased. Further, the above-said features are extracted and trained by generating a multi-fractal ensemble towards various classes using a convolution neural network. The intermediate layers apply a support vector machine toward the classification of an ensemble. The neurons of the intermediate layer apply a support vector machine in estimating Ensemble Centric Coverage Support Measure (ECCSM), Ensemble Centric Mass Support Measure (ECMSM), and Ensemble centric Intensity Support Measure (ECISM) towards various classes. Disease Attraction Weight (DAW), which is measured by the support vector machine using a variety of support metrics, is computed using the estimated values by the method and produced at the output layer. The method carry out disease prediction and estimates survival stage support (SSS) measures to perform survival prediction, as determined by the DAW value. The proposed method improves disease prognostication performance and introduces a lower false ratio.
Keywords: Brain Tumor Classification; CNN; Medical Imaging; Survival Prediction; SVM; RMFEA-CNN Back to TOC