Cited by 484Brain Tumor Detection and ClassificationRadiomics and Machine Learning in Medical ImagingGlioma Diagnosis and Treatment

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

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Gumaei, Abdu, Hassan, Mohammad Mehedi, Hassan, Md Rafiul, Alelaiwi, Abdulhameed, Fortino, Giancarlo·2019·IEEE Access

DOI 10.1109/access.2019.2904145

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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.

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Background
A Hybrid Deep Learning Approach for Brain Tumor Classification
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“In [19], the article proposes to use the wavelet transform-based Ripplet Transform RT.”
Method
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Retracted
MT-MRANBrainTumorNet: A Novel Brain Tumour and Severity Level Classification Framework using Meta-heuristic-assisted Transformer based Multiscale Residual Attention Network
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“The low contrast and low resolution images also degrades the performance of segmentation and hence it is difficult to accurately detect the brain tumors [17].”
Background
Design and development of a deep learning model for brain abnormality detection using MRI
Potadar, M. P., Holambe, R. S., & Chile, R. H. (2023) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2023.2250878 · Cited by 1
“The data from magnetic resonance imaging (MRI), which radiologists routinely use to identify conditions of the brain (Pereira et al. 2016; Gumaei et al. 2019), are utilised in this method.”
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Mzoughi, H., Njeh, I., Slima, M. B., Farhat, N., & Mhiri, C. (2023) · International Conference on Cyberworlds (CW) · doi:10.1109/cw58918.2023.00022 · Cited by 1
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RETRACTED: An efficient hybrid bert model for brain tumor classification
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Retracted
Deep Learning based Autonomous Model for Detection and Classification of Brain Tumor Disease
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“Gumei et al. [8] also deployed extreme learning method for extraction of specific features based on input data after performing pre-processing operation to remove unwanted deterioration with the help of min-max filters and performed identification of abnormal growth of tissues within brain.”
Method
Brain Tumor Classification Based on Deep Learning Algorithms: A Systematic Literature Review
Ramadhani, M. R., Soesanti, I., & Hidayah, I. (2023) · International Conference on Information Technology and Digital Applications (ICITDA) · doi:10.1109/icitda60835.2023.10427361 · Cited by 1
“[34], and Hybrid Learning [17][33][38].”
Automated Classification of MR Brain Images based on Transfer Learning Approach
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“Notably, the Fuzzy C-means algorithm is utilized in the process of feature extraction, contributing to the overall efficacy of the classification system [6].”
Method
Multi-grade Brain Tumor Classification Using a Modified Convolutional Neural Network
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Khoerunnisa, G., Mone, B. V., Nastiti, M. D., Sulistiyo, M. D., & Yunanto, P. E. (2024) · doi:10.1109/icoict61617.2024.10698323 · Cited by 1
A MobileUnet++-based Abnormality Segmentation and Multi-scale Network Approach for Brain Tumor Classification
Kumar, V. P., Pattanaik, S. R., & Kumar, V. S. (2024) · International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS) · doi:10.1109/iacis61494.2024.10721743 · Cited by 1
“The aim is to provide a precise, efficient, and automated means of determining the type and extent of brain tumors, which are essential factors in selecting the most appropriate treatment options [4].”
Background
Brain Tumor Classification Based on Deep Learning Algorithms
Ramadhani, M. R., Soesanti, I., & Hidayah, I. (2024) · International Conference on Electronic and Electrical Engineering and Intelligent System (ICE3IS) · doi:10.1109/ice3is62977.2024.10775314 · Cited by 1
Meta-constrained extreme learning machines: a novel approach for classification problems
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A REVIEW OF DEEP LEARNING AND MACHINE LEARNING TECHNIQUES FOR BRAIN AND BREAST CANCER DETECTION: CHALLENGES AND FUTURE DIRECTIONS
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Similarity Based Characterization and Identification of Breast Ultrasound Lesions in Space-Frequency Subband
Das, P., & Das, A. (2024) · IEEE Region 10 Conference (TENCON) · doi:10.1109/tencon61640.2024.10902967 · Cited by 1
“Finally, lesion discrimination systems are designed using a variety of machine learning methods, including SVM, random forests, decision trees (DT), linear regression, regularized extreme learning machine and others [12,13].”
Hybrid fruit bee optimization algorithm-based deep convolution neural network for brain tumour classification using MRI images
P., A., & Boyed Wesley, A. (2025) · Network Computation in Neural Systems · doi:10.1080/0954898x.2025.2476079 · Cited by 1
Deep learning based automated non-barcoded product identification system for in-person shopping
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“Gumaei et al. [28] introduced the classification of brain tumour using regularised extreme learning machine (RELM).”
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CNN Parameter Adjustment for Brain Tumor Classification
Ho, T. P., & Hoang, V. T. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-2008-9_1 · Cited by 1
Diagnosis of Brain Tumor using Automated Deep Learning Model
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A Hybrid Feature Extraction Method Using SeaLion Optimization for Meningioma Detection from MRI Brain Image
Aswathy, S. U., Stephen, D., Vincent, B., & Prajoon, P. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-030-96299-9_4 · Cited by 1
Advanced Approaches to Brain Tumor Classification and Diagnosis
Jeyalakshmi, G., & Hameed, K. (2022) · International Journal of Electronics and Communication Engineering · doi:10.14445/23488549/ijece-v9i1p102 · Cited by 1
A Coalmine Water Inrush Prediction Model Based on Artificial Intelligence
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Background
Performance Comparison of Classifiers of MR Brain Tumor Images on Various Datasets
Mohan, D., Ulagamuthalvi, V., & Joseph, N. (2022) · IEEE International Conference on Signal Processing, Informatics, Communication and Energy Systems (SPICES) · doi:10.1109/spices52834.2022.9774175 · Cited by 1
Interlayer Textural Variabilities to Study the Benignancy/Malignancy of Brain Tumors using MR Modalities Images
Das, P., & Das, A. (2022) · IEEE Calcutta Conference (CALCON) · doi:10.1109/calcon56258.2022.10060844 · Cited by 1
“[16] proposed an accurate brain tumor classification model using a hybrid feature extraction method with a regularized extreme learning machine (RELM).”
Method
Deep learning based Brain Tumour Classification based on Recursive Sigmoid Neural Network based on Multi-Scale Neural Segmentation
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A Short Literature Review of Brain Tumour Segmentation and Classification Framework: Challenges Issues and Future Scope
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Comprehensive Framework for Classification of Abnormalities in Brain MRI Using Neural Network
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Risk Stratification of Brain lesions in MRI Images using Cascaded Machine Learning Paradigm
Agrawal, U. K. (2021) · Turkish Journal of Computer and Mathematics Education (TURCOMAT)
Deep Convolution Neural Networks Learned Image Classification for Early Cancer Detection Using LightweightPreprint
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Enhanced Frost Self Organizing Map Segmentation Based Gradient Boost Classification for Brain Tumor DetectionPreprint
Kurian, S. M., & Juliet, S. (2022) · Research Square · doi:10.21203/rs.3.rs-1758363/v1
A Deep Learning Model For Brain Tumor Segmentation & Classification Using U-Net & Inception-Net
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TAYLOR-IIWO: TAYLOR IMPROVED INVASIVE WEED OPTIMIZATION-ENABLED DEEP QUANTUM NEURAL NETWORK FOR BRAIN TUMOR SEGMENTATION AND CLASSIFICATION
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Detecting Brain Tumors in Medical Image Technology Using Machine Learning
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Classification of brain tumor using a multistage approach based on RELM and MLBP
Bhavani, M. R., & Vasanth, K. (2023) · EAI Endorsed Transactions on Pervasive Health and Technology · doi:10.4108/eetpht.v8i4.3082
“RELM [24] is a type of feedforward neural networks, which has three main layers such as input, output, and hidden layer.”
Brain tumour Detection Using Deep Models
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A Review of Current Approaches to Automated Detection of Malignant Tumors Based on MR Images
Dixit, R., & Pal, M. K. (2023) · International Conference on Artificial Intelligence and Smart Communication (AISC) · doi:10.1109/aisc56616.2023.10085438
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Background
Survey on Different ML Algorithms Applied on Neuroimaging for Brain Tumor Analysis (Detection, Features Selection, Segmentation and Classification)
Lavanya, K. R., & Bindu, C. S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-27409-1_82
Advancements in Multiclass Brain Tumor Detection and Classification: A Comprehensive Review
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A Novel investigation for a prediction of Intellect tumor and Categorization by CNN based on ResU-Net Architecture
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Brain Hemorrhage Classification Using Leaky ReLU-Based Transfer Learning Approach
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A Comparative Analysis of Deep Learning Models for Brain Tumor Segmentation
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Systematic Report on Exploration of Machine Learning Methods for Brain Tumor Prediction
RajamohanReddy, N., & Muneeswari, G. (2023) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca57980.2023.10220764

Authors: Gumaei, A., Hassan, M. M., Hassan, M. R., Alelaiwi, A., & Fortino, G.
Affiliations: King Saud University (SA)
King Fahd University of Petroleum and Minerals (SA)
University of Calabria (IT)
ORCID: Gumaei, Abdu
Hassan, Mohammad Mehedi
Hassan, Md Rafiul
Alelaiwi, Abdulhameed
Fortino, Giancarlo
Venue: IEEE Access
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2019
Volume: 7
Pages: 36266-36273
ISSN: 2169-3536 (ISSN-L)
DOI: 10.1109/access.2019.2904145
OpenAlex: W2921483513
Cited by: 484
References: 26
Citations by year: 2026: 37 · 2025: 66 · 2024: 105 · 2023: 99 · 2022: 85 · 2021: 54 · 2020: 33 · 2019: 5
FWCI: 19.48
Type: journal article
Language: en
Open access: gold
doi.org
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/OAPA.html (vor)
Locations: IEEE Access (publishedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Archive of Doctoral Theses and Digital Collections (University of Calabria) (submittedVersion)
Funders: Deanship of Scientific Research, King Saud University (RGP-318)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Brain Tumor Detection and Classification
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Health Sciences › Medicine › Oncology › Glioma Diagnosis and Treatment
Keywords: brain tumor classification, hybrid feature extraction, regularized extreme learning machine, brain MRI images, min-max normalization, automated tumor diagnosis, medical image preprocessing

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

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