A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification

By Abdu H. Gumaei, Mohammad Mehedi Hassan, Md. Rafiul Hassan, Abdulhameed Alelaiwi, Giancarlo Fortino. IEEE Access, 2019

376 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Neurology

View Paper: https://www.semanticscholar.org/paper/70d02e3ee6a2537ea4d5982fa4830774737cff46

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Citations in the corpus

MRI-based brain tumor detection using convolutional deep learning methods and chosen machine learning techniques (2023, methodology): “[23] introduced an automated approach to assist radiologists and physicians in identifying different types of brain tumors.”
Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey (2020, background): “reducing the chances of surgery (biopsy) [15], [20]–[22].”
Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network (2020, background): “[30] 5-fold; 80% data in training set, 20% in test 92.”
A Hybrid CNN-SVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images (2021, methodology): “[30] a hybrid technique regularized extreme learning machine (RELM) used to develop an effective brain tumor classifier model.”
Multimodal Brain Tumor Classification Using Deep Learning and Robust Feature Selection: A Machine Learning Application for Radiologists (2020, methodology): “The popular method of diagnosis of these tumors—which spread widely among computer vision researchers—is a computer-aided diagnosis (CAD) system [1,17].”
A review on extreme learning machine (2021, methodology): “Gumaei and Hassan [42] employed a regularized ELM for brain tumor identification.”
MRI-Based Brain Tumor Classification Using Ensemble of Deep Features and Machine Learning Classifiers (2021, methodology): “…to develop a highly accurate and robust solution for MRI-based brain tumor classiﬁcation using various ML classiﬁers: neural network classiﬁer [8,21,64], Naïve Bayes classiﬁer [65], AdaBoost classiﬁer [66], k-NN classiﬁer [64], RF classiﬁer [64,67], SVM classiﬁer [18,22], and ELM classiﬁer [68].”