Optimization driven Deep Convolution Neural Network for brain tumor classification

Abstract The classification and segmentation of the tumor is an interesting area that differentiates the tumorous cells and the non-tumorous cells to identify the tumor level. The segmentation from MRI is a challenge because of its varying sizes of images and huge datasets. Different techniques were developed in the literature for brain tumor classification but due to accuracy and ineffective decision making, the existing techniques failed to provide improved classification. This work introduces an optimized deep learning mechanism; named Dolphin-SCA based Deep CNN, to improve the accuracy and to make effective decisions in classification. Initially, the input MRI images are given to the pre-processing and then, subjected to the segmentation process. The segmentation process is carried out using a fuzzy deformable fusion model with Dolphin Echolocation based Sine Cosine Algorithm (Dolphin-SCA). Then, the feature extraction process is performed based on power LDP and statistical features, like mean, variance, and skewness. The extracted features are used in the Deep Convolution Neural Network (Deep CNN) for performing the brain tumor classification with Dolphin-SCA as the training algorithm. The experimentation is performed using the MRI images taken from the BRATS database and SimBRATS, and the proposed technique has shown superior performance with a maximum accuracy of 0.963.