ArticleComputational intelligence and neuroscience2022
A Novel and Effective Brain Tumor Classification Model Using Deep Feature Fusion and Famous Machine Learning Classifiers.
Article in Computational intelligence and neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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Who cites it
22 citing papers in PubMed, 100 citations in OpenAlex.
- Advancing Tumor Treatment Through Artificial Intelligence and Mathematical Modeling: A Comprehensive Review.Health science reports · 2026Article
- Optimization-Driven Hybrid Machine Learning Framework for Brain Tumor Classification in MRI with Metaheuristic Feature Selection.Diagnostics (Basel, Switzerland) · 2026Article
- LHW-Net: An ensemble-based machine learning framework for brain tumor classification.PloS one · 2026Article
- Hyperspectral imaging for intraoperative brain tumor identification through fusion of spectral, textural, and spectral index features.PloS one · 2026Article
- Article
- Interactive Multi-scale Fusion: Advancing Brain Tumor Detection Through Trans-IMSM Model.Journal of imaging informatics in medicine · 2025Article
- Detection and Localization of Spine Disorders from Plain Radiography.Journal of imaging informatics in medicine · 2024Article
- Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models.Scientific reports · 2024Article
- Utilizing Deep Feature Fusion for Automatic Leukemia Classification: An Internet of Medical Things-Enabled Deep Learning Framework.Sensors (Basel, Switzerland) · 2024Article
- Proximal femur fracture detection on plain radiography via feature pyramid networks.Scientific reports · 2024Article
- Brain Tumor Detection and Categorization with Segmentation of Improved Unsupervised Clustering Approach and Machine Learning Classifier.Bioengineering (Basel, Switzerland) · 2024Article
- Applications of Machine Learning (ML) and Mathematical Modeling (MM) in Healthcare with Special Focus on Cancer Prognosis and Anticancer Therapy: Current Status and Challenges.Pharmaceutics · 2024Review
- Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology.Cancers · 2024Review
- Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm.Frontiers in oncology · 2024Article
- Interpretable machine learning identifies metabolites associated with glomerular filtration rate in type 2 diabetes patients.Frontiers in endocrinology · 2024Article
- Enhancing brain tumor MRI classification with an ensemble of deep learning models and transformer integration.PeerJ. Computer science · 2024Article
- A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features.Sensors (Basel, Switzerland) · 2023Article
- Deep fake detection and classification using error-level analysis and deep learning.Scientific reports · 2023Article
- Role of Ensemble Deep Learning for Brain Tumor Classification in Multiple Magnetic Resonance Imaging Sequence Data.Diagnostics (Basel, Switzerland) · 2023Article
- TumorDetNet: A unified deep learning model for brain tumor detection and classification.PloS one · 2023Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain tumors are difficult to treat and cause substantial fatalities worldwide. Medical professionals visually analyze the images and mark out the tumor regions to identify brain tumors, which is time-consuming and prone to error. Researchers have proposed automated methods in recent years to detect brain tumors early. These approaches, however, encounter difficulties due to their low accuracy and large false-positive values. An efficient tumor identification and classification approach is required to extract robust features and perform accurate disease classification. This paper proposes a novel multiclass brain tumor classification method based on deep feature fusion. The MR images are preprocessed using min-max normalization, and then extensive data augmentation is applied to MR images to overcome the lack of data problem. The deep CNN features obtained from transfer learned architectures such as AlexNet, GoogLeNet, and ResNet18 are fused to build a single feature vector and then loaded into Support Vector Machine (SVM) and K-nearest neighbor (KNN) to predict the final output. The novel feature vector contains more information than the independent vectors, boosting the proposed method's classification performance. The proposed framework is trained and evaluated on 15,320 Magnetic Resonance Images (MRIs). The study shows that the fused feature vector performs better than the individual vectors. Moreover, the proposed technique performed better than the existing systems and achieved accuracy of 99.7%; hence, it can be used in clinical setup to classify brain tumors from MRIs.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.