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A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification
Open accessAbstract
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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“This dataset consists of 15,320 MRI images collected from different sources. The data are accessible upon request only under a license agreement [57].”
From A Comprehensive Analysis of Recent Deep and Federated-Learning-Based Methodologies for Brain Tumor Diagnosis (2022) · cites this paper as [57]
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Cites
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Research works printed but not linked: 1
- cheng, 2017, Brain tumor dataset (version 5)
Cited by
Sorted by citations
Classification of Brain Tumors from MRI Images Using a Convolutional Neural Network
Atanasijević, M. B., & Barjaktarović, M. Č. (2020) · Applied Sciences · doi:10.3390/app10061999 · Cited by 753
“[30] 5-fold; 80% data in training set, 20% in test 92.”
BackgroundSaeedi, S., Rezayi, S., Keshavarz, H., & Kalhori, S. R. N. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02114-6 · Cited by 628
“[23] introduced an automated approach to assist radiologists and physicians in identifying different types of brain tumors.”
MethodResultInfluentialKang, J., Ullah, Z., & Gwak, J. (2021) · Sensors · doi:10.3390/s21062222 · Cited by 641
“…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].”
MethodA review on extreme learning machine
Wang, J., Lu, S., Wang, S.-H., & Zhang, Y.-D. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11007-7 · Cited by 572
“Gumaei and Hassan [42] employed a regularized ELM for brain tumor identification.”
MethodKhairandish, M. O., Sharma, M., Jain, V. K., Chatterjee, J. M., & Jhanjhi, N. Z. (2021) · IRBM · doi:10.1016/j.irbm.2021.06.003 · Cited by 549
“[30] a hybrid technique regularized extreme learning machine (RELM) used to develop an effective brain tumor classifier model.”
Method
Metadata
- 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)
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 36266-36273
- ISSN
- 2169-3536 (ISSN-L)
- 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
- 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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