Brain tumor detection based on Convolutional Neural Network with neutrosophic expert maximum fuzzy sure entropy

•CNN architecture is used as feature extractor to avoid manual feature extraction.•These features are used in various classification (SVM-KNN) algorithms.•The CNN structure was used with Neutrosophy for image processing first time.•A new hybrid method called NS-EMFSE-CNN using segmentation and classification is proposed.•The classification performance of Brain Tumor images with NS-EMFSE-CNN method was higher classical CNN classification. Brain tumor classification is a challenging task in the field of medical image processing. The present study proposes a hybrid method using Neutrosophy and Convolutional Neural Network (NS-CNN). It aims to classify tumor region areas that are segmented from brain images as benign and malignant. In the first stage, MRI images were segmented using the neutrosophic set – expert maximum fuzzy-sure entropy (NS-EMFSE) approach. The features of the segmented brain images in the classification stage were obtained by CNN and classified using SVM and KNN classifiers. Experimental evaluation was carried out based on 5-fold cross-validation on 80 of benign tumors and 80 of malign tumors. The findings demonstrated that the CNN features displayed a high classification performance with different classifiers. Experimental results indicate that CNN features displayed a better classification performance with SVM as simulation results validated output data with an average success of 95.62%.