Paper Info
Title
A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification.
Abstract
Brain cancer classification is an important step that depends on the physician's knowledge and experience. An automated tumor classification system is very essential to support radiologists and physicians to identify brain tumors. However, the accuracy of current systems needs to be improved for suitable treatments. In this paper, we propose a hybrid feature extraction method with a regularized extreme learning machine (RELM) for developing an accurate brain tumor classification approach. The approach starts by preprocessing the brain images by using a min-max normalization rule to enhance the contrast of brain edges and regions. Then, the brain tumor features are extracted based on a hybrid method of feature extraction. Finally, a RELM is used for classifying the type of brain tumor. To evaluate and compare the proposed approach, a set of experiments is conducted on a new public dataset of brain images. The experimental results proved that the approach is more effective compared with the existing state-of-the-art approaches, and the performance in terms of classification accuracy improved from 91.51% to 94.233% for the experiment of the random holdout technique.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2904145
IEEE ACCESS
Keywords
Field
DocType
Brain tumor classification,hybrid feature extraction,NGIST features,PCA,regularized extreme learning machine
Cancer classification,Normalization (statistics),Pattern recognition,Extreme learning machine,Computer science,Brain tumor,Feature extraction,Image segmentation,Preprocessor,Artificial intelligence,Principal component analysis,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
6
PageRank 
References 
Authors
0.47
0
5
Name
Order
Citations
PageRank
Gumaei, A.15310.73
Mohammad Mehedi Hassan2132094.80
Md. Rafiul Hassan391.81
Abdulhameed Al-elaiwi463147.05
Giancarlo Fortino51756155.44