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

Abstract 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%.

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