期刊
MEASUREMENT
卷 147, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2019.07.058
关键词
Brain tumor segmentation; Neutrosophy; 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%. (C) 2019 Elsevier Ltd. All rights reserved.
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