Deep autoencoder-based automated brain tumor detection from MRI data

Abstract

Brain tumor detection from magnetic resonance (MR) image samples is the core way for radiologists, specialists, and physicians. Artificial intelligence-based MR image classification can make a great contribution to the decision-making process of physicians due to both healthcare personnel shortages and workload. Deep learning approaches are frequently used since they have high performance in medical image classification tasks. In this study, a novel and effective method based on a deep autoencoder was proposed for brain tumor detection from MR image samples. In the deep autoencoder structure, convolutional layers were used instead of dense layers. The deep feature sets were obtained from the last encoded layer of the deep autoencoder model. The deep features were reduced with the variance threshold algorithm. A series of classifiers, which are composed of support vector machine (SVM), decision trees (DT), k-nearest neighbor (KNN), and ensembles, were utilized in the classification process. The best classification metrics were provided with the SVM classifier using the radial basis function (RBF) kernel. The achievement of the proposed method was compared to both the existing approaches using the same dataset and the deep learning models such as VGG16 and AlexNet models. © 2022 Elsevier Inc. All rights reserved.