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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RETRACTED ARTICLE: Brain tumor identification and classification of MRI images using data augmented support vector machine
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Background
MR Brain 2D image Tumor and Cyst Classification Approach: an Empirical Analogy
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Brain Tumor Classification Using Medial Residual Encoder LayersPreprint
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“[1] presented a method based on deep ELM networks.”
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Background
Improving Multi-class Brain Tumor Detection Using Vision Transformer as Feature Extractor
Abbasi, A. A., Hussain, L., & Ahmed, B. (2023) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-031-35078-8_1 · Cited by 5
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Method
Brain Tumor Detection using Integrated Learning Process Detection (ILPD)
Praveena, M., & Rao, M. K. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0131018 · Cited by 5
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“It can be applied to a regularization problem by using the variable split in (7) for separable f and g.”
BackgroundInfluential
Analysis of Brain Tumor using Novel Image Processing Approach
Muthukumaran, N., Archana, M., Majitha, A., & GiftaIrine Sophiya., T. (2022) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc54411.2022.9885662 · Cited by 5
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“Gumaei et al. fostered a component extraction strategy utilizing Regularized Extreme Learning Machine (RELM) [1] for ordering various kinds of brain tumors.”
Method
Performance Analysis of Task Offloading in Mobile Edge Cloud Computing for Brain Tumor Classification Using Deep Learning
Yamuna, R., Rajalingam, R., & Rani, M. U. (2023) · Journal of Applied Engineering and Technological Science (JAETS) · doi:10.37385/jaets.v4i2.2164 · Cited by 5
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Method
Extreme Learning Bat Algorithm in Brain Tumor Classification
Sreekanth, G. R., Alrasheedi, A. F., Venkatachalam, K., Abouhawwash, M., & Askar, S. S. (2022) · Intelligent Automation & Soft Computing · doi:10.32604/iasc.2022.024538 · Cited by 5
Neural Memory Plasticity for Anomaly DetectionPreprint
Fernando, T., Denman, S., Ahmedt‐Aristizabal, D., Sridharan, S., Laurens, K. R., Johnston, P., & Fookes, C. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.05448 · Cited by 5
“Hence most approaches in the medical domain have used supervised learning [13, 36, 37, 38].”
BackgroundMethod
A Repeated Median Filtering Method for Denoising Mammogram Images
AlSalman, H. (2020) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2020.0111178 · Cited by 6
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Method
Deep Dive into Brain Tumor Classification: Transfer Learning with Convolutional Neural Networks
Dahri, F. H., Dahri, N. A., Soomro, D. B., Channa, I. A., & Abro, G. E. M. (2024) · International Conference on Innovative Engineering Sciences and Technological Research (ICIESTR) · doi:10.1109/iciestr60916.2024.10798238 · Cited by 4
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Multi-modal MRI data augmentation and attention-based modeling for interpretable brain tumor classification
Alanazi, M. A. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11561-3 · Cited by 4
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Brain Tumor Stages Prediction using FMS-DLNN Classifier and Automatic RPO-RG Segmentation
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EfficientNetB0’s Hybrid Approach for Brain Tumor Classification from MRI Images Using Deep Learning and Bagging Trees
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Method
Identification of Brain Tumor Stages and Brain Tumor Diagnosis Using Deep Learning Model Based on Inception V4 and DENSENET 201
Suresh, M., Saranya, S. S., Punitha, A., & Kowsalya, R. (2023) · International Conference on System, Computation, Automation and Networking (ICSCAN) · doi:10.1109/icscan58655.2023.10395003 · Cited by 4
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Background
Enhancing Brain Tumor Classification in MRI: Leveraging Deep Convolutional Neural Networks for Improved Accuracy
Dip, S. S., Rahman, M. H., Islam, N., Arafat, M. E., Bhowmick, P. K., & Yousuf, M. A. (2024) · International Journal of Information Technology and Computer Science · doi:10.5815/ijitcs.2024.03.02 · Cited by 10
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Brain Tumor Diagnosis Using Hybrid Pre-trained CNN-SVM
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A brain tumor MRI image classification method based on deep DenseNet network
He, X., Ma, K., Huang, J., & He, H. (2024) · International Conference on Image Processing and Artificial Intelligence (ICIPAI) · doi:10.1117/12.3035103 · Cited by 4
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Brain Tumor Classification on MRI Images by using Classical Local Binary Patterns and Histograms of Oriented Gradients
Gottipati, S. B., & Thumbur, G. (2024) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v25i5.2906 · Cited by 4
Automated evaluation and parameter estimation of brain tumor using deep learning techniques
Vijayakumari, B., Kiruthiga, N., & Bushkala, C. P. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10255-6 · Cited by 4
Spectrally distinct pixel extraction and kernel filtering for brain tumour diagnosis
Alhudhaif, A., Alsubai, S., Aseeri, A. O., Nandal, A., & Polat, K. (2024) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2024.106787 · Cited by 4
Swin-HAUnet: A Swin-Hierarchical Attention Unet For Enhanced Medical Image Segmentation
Chen, J., Zhang, X., Li, R., & Zhou, P. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-8496-7_26 · Cited by 4
Adaptive channel attention and multi-path convolutional architecture for brain tumor detection using MRI images
Khan, M. A., & Park, H. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20911-1 · Cited by 4
“For instance, Gumaei et al. [56] utilized a combination of Principal Component Analysis (PCA), NGIST feature extraction, and regularized Extreme Learning Machines (ELMs), signifi - cantly improving classification performance.”
Medial Residual Encoder Layers for Classification of Brain Tumors in Magnetic Resonance Images
Sobhaninia, Z., Karimi, N., Khadivi, P., & Samavi, S. (2022) · doi:10.1109/icee55646.2022.9827434 · Cited by 4
A review on Brain Tumor Detection using Deep Neural Networks
Wani, S., Ahuja, S., & Kumar, A. (2023) · IEEE International Conference on Communication Systems and Network Technologies (CSNT) · doi:10.1109/csnt57126.2023.10134594 · Cited by 4
“In [24] for creating a precise brain tumor classification system, the researchers suggest a hybrid extraction of feature method with regularised extreme machine learning (RELM).”
Method
Brain tumor classification based on deep CNN and modified butterfly optimization algorithm
Jacob, V., Sagar, G. V. R., Goura, K., & Pedalanka, P. S. S. (2023) · Computer Methods in Biomechanics and Biomedical Engineering Imaging & Visualization · doi:10.1080/21681163.2023.2219754 · Cited by 4
“Much research has been conducted in recent years on the topic of identifying cancers that are present in the human brain using pictures from MRI scans (Hamamci et al. 2012; Gumaei et al. 2019; Sultan et al. 2019).”
Background
Feature Extraction and Classification of Gray-Scale Images of Brain Tumor using Deep Learning
Kondra, P., & Vurukonda, N. (2024) · Scalable Computing Practice and Experience · doi:10.12694/scpe.v25i2.2456 · Cited by 4
RE-InCep-BT-:Resource-Efficient InCeptor Model for Brain Tumor Diagnostic Healthcare Applications in Computer Vision
Lamba, K., Rani, S., Khan, M. A., & Shabaz, M. (2024) · Mobile Networks and Applications · doi:10.1007/s11036-024-02320-0 · Cited by 4
Intelligent optimal archimedes shooty tern deep network (OASTDN) for oral squamous cell carcinoma detection and classification in oral cancer
Raj, M. P., & Muneeswari, G. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19398-z · Cited by 4
“After the segmentation process, Principal Component Analysis (PCA) based normalized GIST [26] employsfeature extraction using the Normalized GIST (NGIST) descriptor, which represents a substantial advancement over the standard GIST descriptor.”
A Hybrid-Based Ensemble Learning Model and DNN for Automated Diagnosis of Brain Tumor from MRI Images
Jehangir, B., & Nayak, S. R. (2021) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-7076-3_27 · Cited by 4
Brain Tumor MRI Identification and Classification Using DWT, PCA, and KSVMPreprint
Faruq, M. O., Jahi, I. M., Ahmed, M. S., & Hossain, M. S. (2023) · Research Square · doi:10.21203/rs.3.rs-2562932/v1 · Cited by 4
Application of CSGE-PSPnet Remote Sensing Image Semantic Segmentation Technology in Transmission and Transformation Engineering Design
Li, B., Sun, K., Yao, L., Guan, L., Lu, F., & Zhu, Y. (2022) · Asian Conference on Frontiers of Power and Energy (ACFPE) · doi:10.1109/acfpe56003.2022.9952233 · Cited by 3
“The algorithm is mostly based on traditional machine learning methods, mainly scale invariant feature transformation(SIFT) [4], GIST feature description [10], etc.”
Method
Comparative Analysis of Fine-Tuned MobileNet versions to Detect Brain Tumors from MRI images
Taha, B., Liza, F. R., Moniruzzaman, M., & Samsuzzaman, M. (2022) · International Conference on Sustainable Technologies for Industry 5.0 (STI) · doi:10.1109/sti56238.2022.10103254 · Cited by 3
“Authors of [18] applied PCA-NGIST feature selection method to an ELM extractor to acquire 94.”
Method
A New Breakpoint to Classify 3D Voxels in MRI: A Space Transform Strategy with 3t2FTS-v2 and Its Application for ResNet50-Based Categorization of Brain Tumors
Koyuncu, H., & Barstuğan, M. (2023) · Bioengineering · doi:10.3390/bioengineering10060629 · Cited by 3
Transfer Learning with Pre-trained CNNs for MRI Brain Tumor Multi-Classification: A Comparative Study of VGG16, VGG19, and Inception Models
Nurfarahin, A. A. S., Dziyauddin, R. A., & Norliza, M. N. (2023) · IEEE National Biomedical Engineering Conference (NBEC) · doi:10.1109/nbec58134.2023.10352589 · Cited by 3
“Hossam et al. proposed a model defining tumor type and glioma grades [2], while Gumael et al. used a hybrid technique with regularized extreme learning machines (RELM) for effective brain tumor classification, achieving 94.233% overall accuracy [3].”
MethodInfluential
Detection and Categorization of Brain Tumors Through Deep Learning
Devi, V. A., Bhuvaneswari, E., & Tummala, R. K. (2023) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai59313.2023.10452566 · Cited by 3
A semi-supervised deep neuro-fuzzy iterative learning system for automatic segmentation of hippocampus brain MRI
Nisha, M., Kannan, T., & Sivasankari, K. (2024) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2024344 · Cited by 3
Brain MRI Images Classifications with Deep Fuzzy Clustering and Deep Residual Network
Rajeswari, R., Ganeshan, R., Maram, B., & Cristin, R. (2021) · International Journal of Computational Methods · doi:10.1142/s021987622142007x · Cited by 3
The proposition of Possibilistic sigmoid features and the Shannon-Hanman transform classifier along with the pervasive learning model for the classification of brain tumor using MRI
Asthana, P., Hanmandlu, M., & Vashisth, S. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12482-2 · Cited by 3
“[21] have developed a hybrid feature extraction method using the normalized gist descriptor with Principal Component Analysis (PCA).”
Method
Hybrid Feature Extraction with Ensemble Classifier for Brain Tumor Classification
Leena, B., & Jayanthi, A. N. (2022) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001422500318 · Cited by 3
Distance Metric-based Segmentation and Score-Level Classification for Optimized Tumor Identification in MR Images
Dandu, J. R., Prasath, T. A., & Rajasekaran, M. P. (2022) · IETE Journal of Research · doi:10.1080/03772063.2022.2088628 · Cited by 3
A New Enhanced Local Optimal-Oriented Pattern Descriptor using Steerable Wavelet Transform for MRI Brain Tumor Classification
Patil, D., & Hamde, S. T. (2022) · Iranian Journal of Science and Technology Transactions of Electrical Engineering · doi:10.1007/s40998-022-00557-7 · Cited by 3
Hybrid Segmentation Approach for Tumors Detection in Brain Using Machine Learning Algorithms
Praveena, M., & Rao, M. K. (2023) · International Journal of Image and Graphics · doi:10.1142/s0219467823400089 · Cited by 3
An towards efficient optimal recurrent neural network-based brian tumour classification using cat and rat swarm (CARS) optimisation
Mactina, J. N., & Neduncheliyan, S. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16870-0 · Cited by 3
Segnet with Unet3+ and EfficientNet: a novel framework of brain tumour segmentation and classification model by multiscale attention-based deep learning techniques with hybrid heuristic improvement using 3D MRI brain images
Ramya, D. J., & Lakshmi, C. (2024) · The Imaging Science Journal · doi:10.1080/13682199.2023.2283678 · Cited by 3
“Such as Computed Tomography (CT), Positron Emission Tomography (PET), Magnetic Resonance Imaging (MRI) and so on, are considered [6].”
Dementia Diagnosis through Artificial Intelligence in Medical Images
Mathkunti, N. M., Ananathnagu, U., & Ebin, P. M. (2023) · Global Conference on Information Technologies and Communications (GCITC) · doi:10.1109/gcitc60406.2023.10426234 · Cited by 3
“In the current study, methods such Random Forest [17], CNN [18], and SVM [19] have been applied.”
Method
SPA: Signflip Parallel Analysis to Optimize the Number of Principal Components in Two-dimensional PCA
Li, Z., & Kuang, Y. (2024) · Journal of Data Science · doi:10.6339/24-jds1158 · Cited by 3

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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