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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The Combined dataset consists of 15,320 MRI images collected from different sources; the data are accessible upon request only under a license agreement.
“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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MethodInfluentialDifferential Deep Convolutional Neural Network Model for Brain Tumor Classification
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MethodBTC-fCNN: Fast Convolution Neural Network for Multi-class Brain Tumor Classification
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MethodNawaz, S. A., Khan, D. M., & Qadri, S. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2031824 · Cited by 75 · see citing works
“10% (Gumaei et al. 2019) MRI brain, Gabor, GWT, GIST-descriptor, N-GIST”
MethodInfluentialAsiri, A. A. M., Shaf, A., Ali, T. M., Pasha, M. A., Aamir, M., Irfan, M., Alqahtani, S., Alghamdi, A. J., Alghamdi, A., Alshamrani, A. F. A., et al. (2023) · Sensors · doi:10.3390/s23187913 · Cited by 74 · see citing works
“30% [25] GAN 96% [26] GIST descriptor and ELM 94.”
BackgroundMultimodal brain tumor detection using multimodal deep transfer learning
Razzaghi, P., Abbasi, K., Shirazi, M., & Rashidi, S. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109631 · Cited by 77 · see citing works
Optimization-enabled hybrid deep learning for brain tumor detection and classification from MRI
Kanchanamala, P., Revathi, K. G., & Ananth, M. B. J. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.104955 · Cited by 72 · see citing works
Handcrafted Deep-Feature-Based Brain Tumor Detection and Classification Using MRI Images
Prakash, M., Easwaramoorthy, S. V., Neelakandan, S., Subramanian, M., & Meckanzi, S. (2022) · Electronics · doi:10.3390/electronics11244178 · Cited by 71 · see citing works
Toğaçar, M., Ergen, B., & Cömert, Z. (2020) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-020-02290-x · Cited by 69 · see citing works
“Gumaei et al. [65] processed the dataset in three steps that are preprocessing, feature extraction, and classification.”
BackgroundBrain Tumor Detection Using Machine Learning and Deep Learning: A Review
Lotlikar, V. S., Satpute, N., & Gupta, A. (2021) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/1573405617666210923144739 · Cited by 67 · see citing works
İncir, R., & Bozkurt, F. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111981 · Cited by 69 · see citing works
A Lightweight Convolutional Neural Network Model for Liver Segmentation in Medical Diagnosis
Ahmad, M., Qadri, S. F., Qadri, S., Saeed, I. A., Zareen, S. S., Iqbal, Z., Alabrah, A. A., Alaghbari, H. M., & Rahman, S. M. M. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/7954333 · Cited by 66 · see citing works
“1ese are very powerful methods as compared to machine learning [43], which can acquire more classified features for image segmentation [44] and classification tasks [45–49].”
MethodBhimavarapu, U., Chintalapudi, N., & Battineni, G. (2024) · Bioengineering · doi:10.3390/bioengineering11030266 · Cited by 64 · see citing works
“Gumaei et al. [29] proposed a regularized extreme learning machine (RELM) as a hybrid feature extraction method, including min–max normalization for pre-processing and PCA-NGIST for feature extraction; this method achieved an accuracy of 94.233% with fivefold cross-validation on the T1W-CE MRI…”
MethodInfluentialMultigrade brain tumor classification in MRI images using Fine tuned efficientnet
Priyadarshini, P., Kanungo, P., & Kar, T. (2024) · e-Prime - Advances in Electrical Engineering Electronics and Energy · doi:10.1016/j.prime.2024.100498 · Cited by 66 · see citing works
Mohan, P. P., & Ramkumar, G. (2024) · International Conference on Science Technology Engineering and Mathematics (ICONSTEM) · doi:10.1109/iconstem60960.2024.10568707 · Cited by 58 · see citing works
“This takes a long time and leads to erroneous tumor identification [2][3].”
BackgroundAn Improved Approach for Sentiment Analysis of Arabic Tweets in Twitter Social Media
AlSalman, H. (2020) · International Conference on Computer Applications & Information Security (ICCAIS) · doi:10.1109/iccais48893.2020.9096850 · Cited by 57 · see citing works
“For many years, machine learning methods used in sentiment analysis [3-6] and also in many applications [7-14].”
Krishnapriya, S., & Karuna, Y. (2023) · Health and Technology · doi:10.1007/s12553-023-00737-3 · Cited by 57 · see citing works
“[135] segmentation based on Bayesian Fuzzy clustering, information theoretic, scatters and wavelet features HSC based multi SVNN BRATS 93%”
MethodBMRI-NET: A Deep Stacked Ensemble Model for Multi-class Brain Tumor Classification from MRI Images
Asif, S., Zhao, M., Chen, X., & Zhu, Y. (2023) · Interdisciplinary Sciences Computational Life Sciences · doi:10.1007/s12539-023-00571-1 · Cited by 56 · see citing works
“Gumaei et al. [12] proposed a hybrid feature extraction method to classify brain tumors by using principal component analysis (PCA) with normalized GISTs.”
MethodVankdothu, R., Hameed, M. A., Ameen, A., & Unnisa, R. (2022) · Computers & Electrical Engineering · doi:10.1016/j.compeleceng.2022.108196 · Cited by 54 · see citing works
Rasheed, Z., Ma, Y., Ullah, I., Al‐Khasawneh, M. A., Almutairi, S. S., & Abohashrh, M. (2024) · Bioengineering · doi:10.3390/bioengineering11070701 · Cited by 53 · see citing works
“Although this hybrid method endeavored to capture an inclusive feature set, PCA might not consistently capture the non-linear pattern characteristic in MRI, possibly omitting essential tumor details and resulting in lower accuracy [18].”
MethodInfluentialNeural memory plasticity for medical anomaly detection
Fernando, T., Denman, S., Ahmedt‐Aristizabal, D., Sridharan, S., Laurens, K. R., Johnston, P., & Fookes, C. (2020) · Neural Networks · doi:10.1016/j.neunet.2020.04.011 · Cited by 53 · see citing works
Assiri, A. (2020) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2020.013813 · Cited by 53 · see citing works
“Recently, ML methods have been used for solving many problems in different applications [15,16].”
MethodA novel framework for brain tumor detection based on convolutional variational generative models
Salama, W. M., & Shokry, A. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12362-9 · Cited by 51 · see citing works
“In [12], authors further used two different models for developing an accurate brain tumor classification.”
MethodNeuroimage analysis using artificial intelligence approaches: a systematic review
Bacon, E. J., He, D., Achi, N. A. D., Wang, L., Li, H., Yao-Digba, P. D. Z., Monkam, P., & Qi, S. (2024) · Medical & Biological Engineering & Computing · doi:10.1007/s11517-024-03097-w · Cited by 51 · see citing works
Brain Tumor Classification Using an Ensemble of Deep Learning Techniques
Patro, S. G. K., Govil, N., Saxena, S., Mishra, B. K., Zamani, A. T., Miled, A. B., Parveen, N., Elshafie, H., & Hamdan, M. (2024) · IEEE Access · doi:10.1109/access.2024.3485895 · Cited by 50 · see citing works
“A new brain tumor categorization method was suggested by Gumaei et al. [19].”
MethodLocal dynamic neural network for quantitative analysis of mixed gases
Li, J., Ma, Y., Duan, Z., Zhang, Y., Duan, X., Liu, B., Yuan, Z., Wu, Y., Jiang, Y., & Tai, H. (2023) · Sensors and Actuators B Chemical · doi:10.1016/j.snb.2023.135230 · Cited by 48 · see citing works
Zebari, N. A., Mohammed, C. N., Zebari, D. A., Mohammed, M. A., Zeebaree, D. Q., Marhoon, H. A., Abdulkareem, K. H., Kadry, S., Viriyasitavat, W., Nedoma, J., et al. (2024) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12276 · Cited by 48 · see citing works
Nirmalapriya, G., Agalya, V., Regunathan, R., & Ananth, M. B. J. (2022) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2022.104017 · Cited by 47 · see citing works
A deep learning-based driver distraction identification framework over edge cloud
Gumaei, A., Al-Rakhami, M. S., Hassan, M. M., Alamri, A. M., Alhussein, M. A., Razzaque, M. A., & Fortino, G. (2020) · Neural Computing and Applications · doi:10.1007/s00521-020-05328-1 · Cited by 47 · see citing works
Murthy, N. S., & Bethala, C. (2021) · Journal of Ambient Intelligence and Humanized Computing · doi:10.1007/s12652-021-03147-3 · Cited by 46 · see citing works
Elazab, N., Gab-Allah, W. A., & Elmogy, M. M. (2024) · Scientific Reports · doi:10.1038/s41598-024-54864-6 · Cited by 46 · see citing works
“Their aim is to give clinicians reliable resources for making accurate diagnoses and giving patients prompt, efficient treatment 14,15 .”
BackgroundDeep Learning for Brain Tumor Classification from MRI Images
Arora, S., & Sharma, M. (2021) · International Conference on Image Information Processing (ICIIP) · doi:10.1109/iciip53038.2021.9702609 · Cited by 45 · see citing works
Nguyen-Trong, K., Vu, H. N., Trung, N. N., & Pham, C. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3074642 · Cited by 45 · see citing works
Kakarla, J., Isunuri, B. V., Doppalapudi, K. S., & Bylapudi, K. S. R. (2021) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22554 · Cited by 44 · see citing works
“Gumaei et al 16 brain edges and regions has enhanced using min-max normalization, and then the brain tumor image classification has performed using a regularized ELM.”
MethodRamprasad, M. V. S., Rahman, M. Z. U., & Bayleyegn, M. D. (2022) · IEEE Open Journal of Engineering in Medicine and Biology · doi:10.1109/ojemb.2022.3217186 · Cited by 43 · see citing works
“Moreover, fine-tuning-based transfer learning (FTTL) [33] is used to differentiate meningioma from non-meningioma brain images with a dice coefficient index [34].”
MethodEnhancing brain tumor classification through ensemble attention mechanism
CELIK, F., CELIK, K., & Celik, A. (2024) · Scientific Reports · doi:10.1038/s41598-024-73803-z · Cited by 43 · see citing works
“The regularized extreme learning machine (RELM), introduced by Gumaei et al., is a hybrid feature-extraction method 35 .”
MethodAlshayeji, M. H., Al-Buloushi, J., Ashkanani, A., & Abed, S. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-10927-8 · Cited by 43 · see citing works
Machine learning application in Glioma classification: review and comparison analysis
Bhatele, K. R., & Bhadauria, S. S. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09572-z · Cited by 42 · see citing works
Yıldırım, M., Cengil, E., Eroğlu, Y., & Çınar, A. (2023) · Iran Journal of Computer Science · doi:10.1007/s42044-023-00139-8 · Cited by 42 · see citing works
“They stated that they preferred pre-trained CNN models due to the small number of data in the Gumaei and colleagues [22] used the RELM extreme learning machine for feature extraction.”
MethodGated Deep Reinforcement Learning With Red Deer Optimization for Medical Image Classification
Ganesh, N., Jayalakshmi, S. L., Narayanan, R. C., Mahdal, M., Zawbaa, H. M., & Mohamed, A. W. (2023) · IEEE Access · doi:10.1109/access.2023.3281546 · Cited by 41 · see citing works
“Authors in [10] suggested a hybrid feature extraction methodwith a regularized extreme learningmachine (RELM) to enhance accurate classification.”
BackgroundSachdeva, J., Sharma, D., & Ahuja, C. K. (2024) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-023-10041-y · Cited by 40 · see citing works
Musthafa, N., Memon, Q. A., & Masud, M. M. (2025) · Eng—Advances in Engineering · doi:10.3390/eng6050082 · Cited by 40 · see citing works
“…this, benchmarking and regulatory e ff orts accelerated to bridge the clinical translation of these e ff orts. few research studies addressed multiple views of MRI images (shown in Figure 2) and exploited them for progression of tumor stages and thus helped the physician plan treatment accordingly.”
Gumaei, A., Sammouda, R., Al-Rakhami, M. S., AlSalman, H., & El‐Zaart, A. (2021) · Health Informatics Journal · doi:10.1177/1460458221989402 · Cited by 40 · see citing works
Wong, K. K. L., Xu, W., Ayoub, M., Fu, Y.-L., Xu, H., Shi, R., Zhang, M., Su, F., Huang, Z., & Chen, W. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107602 · Cited by 39 · see citing works
A Hybrid Learning-Architecture for Improved Brain Tumor Recognition
Dixon, J., Akinniyi, O., Abdelhamid, A., Saleh, G. A., Rahman, M. M., & Khalifa, F. (2024) · Algorithms · doi:10.3390/a17060221 · Cited by 39 · see citing works
- 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
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
- Health Sciences › Medicine › Genetics › 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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