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

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

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

Open access

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

DOI 10.1109/access.2019.2904145

Open this paper in CoChat — free account Open PDF

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.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.1109/access.2019.2904145

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Extreme learning machine: Theory and applications
Huang, G.-B., Zhu, Q.-Y., & Siew, C.-K. (2006) · Neurocomputing · doi:10.1016/j.neucom.2005.12.126 · Cited by 13,285
Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope
Oliva, A., & Torralba, A. B. (2001) · International Journal of Computer Vision · doi:10.1023/a:1011139631724 · Cited by 6,321
Extreme Learning Machine for Regression and Multiclass Classification
Huang, G.-B., Zhou, H., Ding, X., & Zhang, R. (2011) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/tsmcb.2011.2168604 · Cited by 5,575
Two-dimensional pca: a new approach to appearance-based face representation and recognition
Yang, J. J., Zhang, D. D., Frangi, A. F., & Yang, J.-Y. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.1261097 · Cited by 3,565
Brain Tumor Segmentation Using Convolutional Neural Networks in MRI Images
Pereira, S., Pinto, A., Alves, V., & Silva, C. A. (2016) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2016.2538465 · Cited by 2,735
Image representation using 2D Gabor wavelets
Lee, T. S. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.541406 · Cited by 1,632
Optimization method based extreme learning machine for classification
Huang, G.-B., Ding, X., & Zhou, H. (2010) · Neurocomputing · doi:10.1016/j.neucom.2010.02.019 · Cited by 869
Review of MRI-based brain tumor image segmentation using deep learning methods
Işın, A., Direkoğlu, C., & Şah, M. (2016) · Procedia Computer Science · doi:10.1016/j.procs.2016.09.407 · Cited by 766
Brain Tumor Classification Using Convolutional Neural Network
Abiwinanda, N., Hanif, M., Hesaputra, S. T., Handayani, A., & Mengko, T. R. (2018) · World Congress on Medical Physics and Biomedical Engineering, September 7 - 12, 2009, Munich, Germany · doi:10.1007/978-981-10-9035-6_33 · Cited by 677
Classification of CT brain images based on deep learning networks
Gao, X. W., Hui, R., & Tian, Z. (2016) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2016.10.007 · Cited by 366

Flagged first, then by citations

Show all 26 references

Research works printed but not linked: 1

cheng, 2017, Brain tumor dataset (version 5)

Sorted by citations

Items 401–484 of 484

An Efficient Pre-Trained Classification of Brain Tumor with Convolutional Neural Networks
Sangeetha, G., & Vadivu, G. (2023) · International Conference on Recent Trends in Data Science and its Applications · doi:10.13052/rp-9788770040723.028
K Nearest Neighbour and Improved Artificial Neural Network Techiniques for Student Academic Performance Prediction
Ali, N. M. F., Elamparithi, M., & Anuratha, V. (2023) · International Journal of Membrane Science and Technology · doi:10.15379/ijmst.v10i2.3054
“The values in the dataset are normalized within the given range minimum and maximum value from dataset and each value are replaced according to the following formula[13].”
Method
Analysis and development of brain tumor prediction model using deep neural network
Chhabra, S., & Bansal, K. (2023) · ITM Web of Conferences · doi:10.1051/itmconf/20235701001
“In clinical brain anatomy research, MRI has become a crucial technique as the general clear separation of soft tissue, sharp contrast, and high resolution a valid diagnosis might well be established [8].”
Background
A deep learning-based intelligent decision-making model for tumor and cancer cell identification
Durga, P., & Godavarthi, D. (2023) · Bulletin of Electrical Engineering and Informatics · doi:10.11591/eei.v13i1.6469
“[20] created an enhanced approach to extract the characteristics by employing the regularized extreme learning machine (RELM).”
BackgroundMethodInfluential
An Efficient ML Approach to Detect Brain Tumor Using MRI Images
Muktadir, M. A., Ullah, A. S. M. R., Islam, M. J., Hossain, M. E., Munny, T. A., Ali, S. M., & Alam, M. A. (2023) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit60459.2023.10441645
“Gumaei, Hassan et al.[9] proposed a method that consists three steps for classification of brain tumors.”
Method
Classify Parkinson Disease from MRI Sample Based on Hybrid Feature Extraction Method
Sultana, Z., Islam, M. S., & Tasnim, F. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-99-8937-9_27
Optimizing Brain Tumour Classification: A Fusion Approach of PCA, SVM, and Image-Based Features
Dheepak, G., Johnvictor, A. C., & Vaishali, D. (2023) · Global Conference on Information Technologies and Communications (GCITC) · doi:10.1109/gcitc60406.2023.10426372
Enhancing Brain Tumor Classification: A CNN-Based Approach with InceptionV3 and Xception
Shankar, G. B., & Dhongade, P. V. S. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-18174
Spa: Signflip Parallel Analysis to Optimize the Number of Pcs in Two-Dimensional PcaPreprint
Li, Z., & Kuang, Y. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4831597
DTDO: Driving Training Development Optimization enabled deep learning approach for brain tumour classification using MRI
Prasad, V. V. K. D. V., Jingle, I. D. J., & Sriramakrishnan, G. V. (2024) · Network Computation in Neural Systems · doi:10.1080/0954898x.2024.2351159
“By assuming the data as 90%, existing methodologies received the FNR as 0.183 for RELM, 0.144 for CNN, 0.103 for transfer learning, 0.128 forModified U-Net structure-residual networks, 0.094 for Enhanced learning machine, and 0.096 for WHHO-DeepCNN, whereas, the proposed mode gained the FNR of 0.073.”
Influential
Dual-input Dual-stream Network with Two-fold Margin Loss for Improved Brain Tumor Detection on MRI ImagesPreprint
Tole, K., Kaloi, M. A., Ali, R., Hajano, N. H., Chan, A. S. S., Babbar, I. A., & Ali, A. (2024) · Preprints.org · doi:10.20944/preprints202406.0242.v1
OPTIMIZED U-NET SEGMENTATION MODEL AND DEEP MAXOUT CLASSIFIER FOR BRAIN TUMOR CLASSIFICATION
Thomas, S., & Sudarmani, R. (2024) · Biomedical Engineering Applications Basis and Communications · doi:10.4015/s1016237224500273
Comparative Analysis of Deep Learning Frameworks for the Classification of Brain Tumors
Malleswari, P. N., Parasa, S. R., Pullarao, M. V., Dhanalakshmi, V., & Sravani, T. D. (2024) · International Conference on Social and Sustainable Innovations in Technology and Engineering (SASI-ITE) · doi:10.1109/sasi-ite58663.2024.00039
Implementation of Morphological Fractional Order Darwinian Operator for Brain Tumour Localization
Ansah, K., Adevu, W. B., Mensah, J. A., & Appati, J. K. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-72483-1_13
Optimizing Feature Selection for Brain Tumor Classification using a Hybrid ACCSA and Utilizing Machine Learning Algorithms for classification
Nageshwaran, V. (2024) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec61587.2024.10722749
Breakthrough in Brain Tumor Diagnosis: A Cutting-Edge Hybrid Depthwise-Direct Acyclic Graph Network for MRI Image Classification
Joseph, X. F., Vijayakumar, M. D., Therese, P. S., Shermila, P. J., Eugine Prince, M., & Maris Murugan, T. (2024) · Journal of Applied Engineering and Technological Science (JAETS) · doi:10.37385/jaets.v6i1.5938
“(Gumaei et al., 2019) have introduced a current, distilled set of significant features from the original GIST features that prevents overfitting and preserves the classification step.”
Towards Smart Hybrid Feature Analysis and Deep Neuroevolutionary Brain Tumour Prediction Based on Transfer Learning
Somuri, J., Muppidi, N., Krishna, C. J., Sasetty, S., & Suresh, S. (2024) · Lecture notes in networks and systems · doi:10.1007/978-981-97-4892-1_22
Brain Tumor Classification from MRI Scans Using a Novel CNN Architecture and Optimization Techniques
Hussain, S. J., Devi, B. R., Mahalakshmi, V. J., Harikala, Parveen, S., & Athinarayanan, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-73477-9_9
Natural Landmark Feature Extraction and Memorization with Depth Camera
Noventino, T. H., Rusdinar, A., & Cahyadi, W. A. (2024) · International Conference on Smart Mechatronics (ICSMech) · doi:10.1109/icsmech62936.2024.10812241
Categorization of Brain Tumor Using CNN from 2D MRI Scan Images
Morankar, G., Harikrishnan, R., & Pacharaney, U. S. (2024) · International Conference on Integrated Intelligence and Communication Systems (ICIICS) · doi:10.1109/iciics63763.2024.10859747
An Extensive Review on Deep Learning Based Approaches for Brain Tumor Classification
Ghosh, D., & Devi, G. U. (2025) · Smart innovation, systems and technologies · doi:10.1007/978-981-97-8096-9_3
A Cryptography-Based Framework for Securing Medical Imaging Data in Robotic Systems
Arshad, H., Iqbal, M., Mir, A. W., & Fatima, R. (2024) · International Conference on Robotics and Automation in Industry (ICRAI) · doi:10.1109/icrai62391.2024.10894533
“An ROC curve is a visual representation exhibiting the performance of a relegation model at all relegation thresholds as given in [16].”
An Efficient Image Classification Technique Using Transfer Learning Mechanism in the Context of Tumor Detection
Sengupta, A., Pal, A. R., & Dasgupta, A. (2025) · International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT) · doi:10.1109/icaect63952.2025.10958960
A Modified Chicken Swarm Optimisation Algorithm for Feature Selection of a Multi-Textural Feature Digital Image Classification System
Agboola, F. F., Oladimeji, I. W., O, O. I., Adeleye, F. S., & Ismaila Folasade, M. (2025) · doi:10.38124/ijisrt/25mar2010
Identification of Brain Tumor Using Segmentation and Classification Techniques: A Systematic Review
Singh, M. P., Singh, T., & Dixit, P. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-81080-0_9
Comparative Study of Different Models on MRI-Based Brain Tumor Detection
Raj, A., Rawat, D., Verma, D., Pandey, S., Rana, D., & Verma, G. (2025) · International Conference on Cognitive Computing in Engineering, Communications, Sciences and Biomedical Health Informatics (IC3ECSBHI) · doi:10.1109/ic3ecsbhi63591.2025.10991161
“Badža and Barjaktarović [21] also achieved an accuracy of 96.56% using CNN with some no. of k-fold cross-validation.”
Influential
Hybrid Deep Learning Model for Robust Brain Tumor Detection: Integrating Filtering and Feature Extraction
Shyamala, N., & Basha, S. M. (2025) · International Conference on Trends in Electronics and Informatics (ICOEI) · doi:10.1109/icoei65986.2025.11013737
Brain tumor detection based on commutable fruit fly algorithm-based ECNN using MRI images
Byeon, H., Kavitha, R. J., Meenakshi, D., & Tonk, A. (2025) · AIP conference proceedings · doi:10.1063/5.0275944
Application of AI to Detect Brain Tumors
Kalita, K., Mall, P., & Raina, D. (2025) · Springer eBooks · doi:10.1007/978-981-96-4241-0_15
Enhanced Brain Tumor Classification using Ensemble Fusion Method
Puja, R. S., & Yousuf, M. A. (2024) · International Conference on Computing Advancements (ICCA) · doi:10.1145/3723178.3723301
“A useful tool to assist specialists in selecting the most appropriate course of treatment is an automated categorization system for brain tumors [14].”
Brain Tumor Classification Leveraging SWIN Transformer with Hybrid Whale Remora Optimization Based Hierarchical VGGNet19
Krishna, P. R., Prasad, V. V. K. D. V., & Krishna, B. T. (2025) · International Journal of Image and Graphics · doi:10.1142/s0219467827500422
Hybrid Q-Learning with VLMs Reasoning Features
Ashraf, M. S., Akuthota, V., Prapty, F. T., Sultana, S., Riad, M. J. A., Ghosh, C. R., Hasan, N., & Anwar, A. S. (2025) · International Conference on Artificial Intelligence and Machine Learning Applications Theme: Healthcare and Internet of Things (AIMLA) · doi:10.1109/aimla63829.2025.11040757
Multilevel Machine Learning Model for Brain Cancer Tumors Diagnoses
Abdalla, M., Sayed, F. R., Tawab, A. H. S. A., & AbdelAziz, A. M. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-3253-4_52
Machine Learning Approach for MRI Brain Tumor Detection using Regularized Extreme Learning
Agrawal, R. K., Alam, M. S., Rajesh Kumar, A., Aparna, N., Gayathri Devi, S., & Jain, A. (2025) · International Conference on Data Science and Information System (ICDSIS) · doi:10.1109/icdsis65355.2025.11070823
“An aberrant proliferation of brain cells that impacts neurological system functions is the cause of a brain tumor, one of the most dangerous disorders [1].”
Feature selection and classification of brain midline tumors using tiny vision transformers and neighborhood component analysis
Demiroğlu, U., & Şenol, B. (2025) · International Advanced Researches and Engineering Journal · doi:10.35860/iarej.1635430
Convolutional Neural Network-based Framework for Brain Tumor Classification and Segmentation using Magnetic Resonance Images
Kathuria, A., Gupta, D., & Uppal, M. (2025) · Journal of Visualized Experiments · doi:10.3791/68428
Leveraging Deep Learning and Classifier Ensembles for Brain Tumor Classification
Dey, I., & Pratap, V. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6124-4_45
Diagnosis and Prediction of Brain Tumor Using Artificial Intelligence
Kumar, A., & Singh, D. (2025) · Transactions on computer systems and networks · doi:10.1007/978-981-96-6703-1_3
Prediction of Glioblastoma Using 3D-CNN
Rahoman, M. A., Shakil, M., Islam, M. S., Morshed, M. A., & Reza, A. W. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-6046-9_28
Detection and Classification of Brain Tumor Based on Convolutional Deep Learning Techniques
Ramya, K., Sai, N. V. V., Mani, K. J., Sekhar, M. R., & Ayyappan, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-3-031-78922-9_37
A Review of Artificial Intelligence Techniques for Brain Tumour Segmentation and Classification
Babu, B. R., Rajesh, V., Inthiyaz, S., Rao, K. S., & Sravan, V. S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-7807-5_9
Infrared thermal radiation image inspection based on optical network transmission application in cross border E-commerce product selection management
Liu, X. (2025) · Journal of Radiation Research and Applied Sciences · doi:10.1016/j.jrras.2025.102048
An Efficient CNN-Based Approach for Binary Classification of Brain Tumors
Rai, N., & Sakalle, V. (2025) · International Conference on Next Generation of Green Information and Emerging Technologies (GIET) · doi:10.1109/giet65294.2025.11234827
“By employing a meticulous fivefold cross-approval technique, the RELM procedure produced a notable 92.61% overall accuracy [37].”
Influential
Brain Disease Detection Using Image Processing and Machine Learning: A Review
Rai, N., & Sakalle, V. (2025) · International Conference on Next Generation of Green Information and Emerging Technologies (GIET) · doi:10.1109/giet65294.2025.11234824
Analysis of Deep Learning and Machine Learning for Brain Tumor Classification
Awasthi, R., & Bhardwaj, A. (2025) · doi:10.1109/cises66934.2025.11265556
“Sajid Iqbal et al.[3] explains the application of computer assistance explored MRI features intended for brain tumor distinction.”
Brain‐RetinaNet: Detection of Brain Tumour Using an Improved RetinaNet in Magnetic Resonance Imaging
Iqbal, R., Mahum, R., Sharaf, M. A. F., & Khan, J. A. (2025) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.70040
Bridging Radiology and AI: Intelligent Brain Tumor Detection and Classification Using Advanced Deep Learning Frameworks
Mythili, K., Sangeetha, T., & Muthu Lakshmi, S. (2025) · International Conference on Sustainability, Innovation & Technology (ICSIT) · doi:10.1109/icsit65336.2025.11294911
Brain Tumour Detection Using Contour-Based Cropping and Skull Stripping
Sharma, P., & Keserwani, P. K. (2026) · Lecture notes in networks and systems · doi:10.1007/978-981-96-9239-2_13
Hybrid Filter Denoising and Dehazing for Enhancing Low-Quality Images
Veeramani, K., & Krishnamurthy, V. (2025) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898810337125010007
Enhanced brain tumor classification using augmented data: A study of custom CNN and Mobilenetv2 architectures
Gangadaran, A. (2026) · AIP conference proceedings · doi:10.1063/5.0298373
MRI-Based Brain Tumour Diagnosis Using Deep Learning
Panda, P., & Das, S. (2026) · BENTHAM SCIENCE PUBLISHERS eBooks · doi:10.2174/9798898812881126010006
Enhancing Brain Tumor Classification Through Image Subtraction Analysis
Mishra, M. R., Nayak, J., Tripathy, A. A., Jena, S., Bhui, M. R., & Meher, P. K. (2025) · International Conference on Cognitive, Green and Ubiquitous Computing (IC-CGU) · doi:10.1109/ic-cgu67042.2025.11338093
“Gumaei et al. [7] have proposed a hybrid approach that combines multiple feature extraction methods with a regularized extreme learning machine to improve classification performance.”
Hybrid CNN-HOG Feature Fusion with Attention Mechanisms and SVM for Accurate Brain Tumor Diagnosis
Mager, H., Amalraj, C. R. J., & G.Kaushalya (2025) · International Conference on Information Technology Research (ICITR) · doi:10.1109/icitr69413.2025.11353688
Contrast-enhanced MRI segmentation and classification with optimized vector machine classifier
Asiri, A. A. M., Soomro, T. A., Ubaid, F. B., Jandan, F. A., Irfan, M., Mehdar, K. M., Alelyani, M., Alshuhri, M. S., Alghamdi, A., & Alamri, S. (2026) · PeerJ Computer Science · doi:10.7717/peerj-cs.3482
Multiclass Brain Tumor Classification using Optimization Enabled Deep Learning
Jarria, S. P. A., & Wesley, A. B. (2025) · International Conference on NexGen Networks and Cybernetics (IC2NC) · doi:10.1109/ic2nc67409.2025.11376428
“…is denoted as, ( ) In relation to local intensity variations, LGB acts as invariant and is denoted by the terms 8 . i) Hybrid PCA-NGIST Hybrid PCA-NGIST [24] is derived from the benefits of PCA model with GIST descriptor and is considered as PCA-based normalized GIST feature extraction technique.”
MRI-Based Brain Tumor Detection Using CNN and Ensemble Methods
Kumar, J., Gupta, S., & Singh, M. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-981-95-6063-9_46
Data preprocessing and augmentation techniques for neuroinformatics
Illakiya, T., Anand, S., Vidhya, B., & Chelladurai, S. P. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41459-6.00013-4
Deep learning techniques for brain tumor detection and classification: trends, challenges, and future directions
Santhosh, A. J. S., Lingaswamy, S., & Ramamurthy, K. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-41459-6.00006-7
Comprehensive Insights Into CNN-based Models for Brain Tumor Identification
Chandan Raj, B. R., Ankitha, T., Moneesh, S. S., Pvn, S. Y., & Manjunath, Y. (2026) · International Conference on Communication, Computing and Electronics Systems (ICCCES) · doi:10.1109/iccces62661.2026.11436856
Retrospection dropout bare-bones particle swarm optimization for feature-based brain tumor classification in MRI images
Chen, W., Shi, T., Guo, J., & Fu, L. (2026) · Frontiers in Oncology · doi:10.3389/fonc.2026.1738658
A hybrid optimized framework with energy shape prior segmentation for brain tumor detection in MRI images
Dutta, A. K., Bokhari, Y., Shaikh, Z. A., YOUSEF, A., Alsubai, S., Alzahrani, M. G., Anjum, M., & Shahab, S. (2026) · Digital Health · doi:10.1177/20552076261418837
Campus Lifestyle Analytics and Academic Success Using IoT-Based Behavioural Tracking
Swarnalatha, G., Mohan, S., & Saabakarini, G. (2026) · International Conference on Electronic Systems and Intelligent Computing (ICESIC) · doi:10.1109/icesic67389.2026.11496423
“Such patterns, which were previously unseen, are now easily identifiable [3].”
Toward Scalable Brain Tumor Diagnosis through Ensemble Robustness and Lightweight Knowledge Distillation from MRI
Khansur, M. N., & Ahmed, B. (2025) · International Conference on Computer and Information Technology (ICCIT) · doi:10.1109/iccit68739.2025.11490282
“Hybrid methods such as Gumaei et al. [12] combined traditional and modern approaches, improving accuracy to 94.23% but still underutilizing MRI spatial context.”
Influential
VGG16-Based Brain Tumor Detection with Explainable AI
Shaikh, Z. M., Baig, M. A., Mirza, M. S., & Usmani, I. A. (2026) · Global Conference on Wireless and Optical Technologies (GCWOT) · doi:10.1109/gcwot69191.2026.11499665
Efficient Brain Tumor Detection Using Deep Learning Models: A Comparative Analysis of CNN and Transfer Learning
Masud, M. M. H., & Rahman, M. S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-15764-5_2
Enhancing Lung Nodule Prediction Accuracy: A Data‐Driven Approach With Dynamic Feature Extraction
Maqbool, M., Awais, M., Tarif, A., Zafar, N. A., Shim, S., Hussain, L., Waheed, A., & Nadeem, M. A. (2026) · Journal of Engineering · doi:10.1155/je/1367255
Hybrid CNN Framework Leveraging VGG16 for Multi-Class Brain Tumor Classification
Ahmed, M. E., Sayem, M. S. U., Piash, A. H., & Akter, A. (2025) · IEEE International Women in Engineering (WIE) Conference on Electrical and Computer Engineering (WIECON-ECE) · doi:10.1109/wiecon-ece69386.2025.11526394
“In [5], the authors introduced a hybrid feature extraction technique utilizing a regularised extreme learning machine (RELM) and formulated a precise brain tumor classification algorithm.”
Tumor-Swin Transformer model for brain Tumor classification using MRI
Shahzad, T., Saqib, S. M., Mazhar, T., Jiang, W., & Ouahada, K. (2026) · Array · doi:10.1016/j.array.2026.100954
Deep Feature Extraction and PCA–Relief Optimization for Multi-Class Brain Tumor Classification
Chihnitha, B., Malladi, V., Alluri, G. V., Mangalagiri, L. S., & Mahamkali, N. (2026) · International Conference on Emerging Trends in Advancements and Applications of Computational Intelligence Techniques (ETAACT) · doi:10.1109/etaact69135.2026.11542144
A Global-Local Interaction Modeling Network with Adaptive Feature Optimization for Brain Tumor Classification Using MRI
salma, S., Lingareddy, S. C., Shilpashree, S., & Kumar, V. (2026) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.18843
“[25] 94.82 Hybrid CNN [26] 98.4 XGBoost [27] 88.51 Ensemble of deep features with SVM [28] 93.72 Regularised Extreme Learning Machine [29] 94.33 Multi-level attention network [30] 96.51 CGAN [31] 97 Deep-GAN [32] 96 Deep-EFNet [33] 3K-DS 95.”
Influential
Enhanced brain tumour detection using fusion of ConvNeXtTiny with stacking generalization
Lanjewar, M. G., Kumar, M. V., Panchbhai, K. G., Parate, R. K., Patle, L. B., & Charanarur, P. (2026) · Discover Computing · doi:10.1007/s10791-026-10202-0
“Gumaei et al. [18] suggested a hybrid feature extraction strategy for BT categorization using a regularised extreme learning machine (RELM).”
Smart Brain Tumor Segmentation and Classification Using Inception ResNet‐V2 and Eagle Eye Vision in IoMT
Ashwini, A., Chirchi, E. R. M., Kumari, V. S., Senthilvel, P. G., & Yimam, F. A. (2026) · Applied Computational Intelligence and Soft Computing · doi:10.1155/acis/2622873
Enhanced Brain Tumor Detection with Laplacian Pyramid based Image Super-Resolution
Tiwari, N. K., & Rajput, S. S. (2026) · Procedia Computer Science · doi:10.1016/j.procs.2026.06.094
EfficientNet-Based Deep Learning for MRI Brain Tumor Classification
Chamakh, C., Dakir, I., hadfi, Y. E., & Marzaq, K. (2026) · Springer proceedings in mathematics & statistics · doi:10.1007/978-3-032-22352-4_6
AF-MRNet: Saliency-Aware Frequency Fusion and Mask-Guided Multi-Scale Attention for Robust Brain Tumor Classification
Xu, Q. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3513-1_2
Interpretable Deep Learning for Automated MRI-Based Brain Tumor Diagnosis Using MobileNet and DenseNet
Pawanja, R., Carpena, J., Chan, R., Pham, H. T., Al-Hussaeni, K., & Khokhar, R. H. (2026) · IEEE/ACIS International Conference on Software Engineering Research, Management and Applications (SERA) · doi:10.1109/sera69989.2026.11618592
“The results highlighted [18] 94.23% 2 Badza et al., 2020 [23] 95.40% 3 Huang et al., 2020 [20] 95.49% 3 Fernand et al., 2020 [22] 95.50% 3 Swati et al., 2019 [17] 96.13% 3 Deepak et al., 2019 [16] 98.00% 3 Redman et al., 2019 [15] 98.69% 3 Our Method 98.71% 3 that CNNs are a powerful approach for…”
Influential
Threshold-Optimized and Explainable Deep Learning Model for Brain Tumor Detection Using MRIz
Tyagi, K., Kandpal, L., Yadav, R., Panwar, R., & Bhardwaj, B. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-28310-8_49
An optimized approach for brain tumor classification using MobileNet and enhanced Newton-Raphson optimizer
Houssein, E. H., Helmy, B. E.-D., Elngar, A. A., Taha, A., & Shaban, H. (2026) · Journal Of Big Data · doi:10.1186/s40537-026-01529-y
FIBONACCINET: A BIOLOGICALLY-INSPIRED DEEP LEARNING ARCHITECTURE FOR ACCURATE AND EXPLAINABLE BRAIN TUMOR CLASSIFICATION FROM MRI
Kılıç, Ş. (2026) · Konya Journal of Engineering Sciences · doi:10.36306/konjes.1681818
CBAM-Based-HybridCNN-AutoEncoder Model for Multiclass Classification of Brain Tumor Images
Dusari, S. T., Ailuri, S., Sivashankar, G., & Devabattini, S. (2026) · Smart innovation, systems and technologies · doi:10.1007/978-981-95-9097-1_24
Super-Resolution and Artificial Transformer with Attention for accurate detection of brain tumor in MRI-based medical imagingPreprint
Dwivedi, S. S. (2026) · Research Square · doi:10.21203/rs.3.rs-8157315/v1
Automated Brain Tumour Classification and Area Quantification Using a Classical Image Processing Framework with Random Forest Model
Jain, I., Kamath, Y., & Agrawal, S. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-30954-9_39
Fine Tuning DenseNet121 for Multi-Class Brain Tumor MRI Classification Using Adaptive Regularization
Anitha, Y., Prakash, R. V., & Dubey, R. S. (2026) · International Conference on Advancements in Smart, Secure and Intelligent Computing (ASSIC) · doi:10.1109/assic70037.2026.11717233
“Gumaei et al. created a novel method for categorising brain tumours [9].”
Brain Tumor Detection with Advanced Approach Using CNN Algorithm
Bhatia, S., Thakur, C., Shilpa, Gupta, S., Sahana, M. P., & Aman (2026) · doi:10.1109/incect72370.2026.11742890
“The various layers of convolution operation in CDL models are capable of extracting most of the visual features like shapes, texture and the complex information of spatial information which can help to achieving precise detection and classification of tumor [4].”

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks