A Novel Deep CNN Model for Classification of Brain Tumor from MR Images

Abstract

the segmentation of brain tumor and its classification in the early stage is very important for the purpose of diagnosis and treatment. This work introduces a new deep neural network model Lu-Net with less layers, less complexity and very efficient for identifying tumors. The work involves classifying brain magnetic resonance (MR) images from a dataset of 253 images of high pixels into two categories of tumors and non-tumors. MR images are initially resized, cropped, preprocessed, and augmented for accurate and rapid training of deep convolutional neural network (CNN) models. The performance of the Lu-Net model has been evaluated using five types of statistical evaluation matrix accuracy, recall, specificity, F-score and accuracy, and its performance also compared with other two types of model Le-Net and VGG-16. CNN models were trained and evaluated on augmented dataset and tested on untrained datasets. The overall accuracy of Le-Net, VGG-16 and the proposed model is 88%, 90% and 98%, respectively, indicating the superiority of the proposed model.