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Hybrid approach for brain tumor detection and classification in magnetic resonance images

https://doi.org/10.1109/ccintels.2015.7437900
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/jmri.1880050520openalex-snapshot
Unsupervised Measurement of Brain Tumor Volume on MR Images
inferredcites →https://doi.org/10.1007/11569541_47openalex-snapshot
Segmenting Brain Tumors with Conditional Random Fields and Support Vector Machines
inferredcites →https://doi.org/10.1007/978-3-540-39899-8_70openalex-snapshot
Interactive, GPU-Based Level Sets for 3D Segmentation
inferredcites →https://doi.org/10.1016/0730-725x(93)90205-ropenalex-snapshot
VIII. MR image texture analysis—An approach to tissue characterization
inferredcites →https://doi.org/10.1016/0730-725x(94)00124-lopenalex-snapshot
MRI segmentation: Methods and applications
inferredcites →https://doi.org/10.1016/0730-725x(95)00012-6openalex-snapshot
Comparison of supervised MRI segmentation methods for tumor volume determination during therapy
inferredcites →https://doi.org/10.1016/j.compmedimag.2011.06.001openalex-snapshot
Quick detection of brain tumors and edemas: A bounding box method using symmetry
inferredcites →https://doi.org/10.1016/j.ejrad.2005.03.028openalex-snapshot
Semi-automated brain tumor and edema segmentation using MRI
inferredcites →https://doi.org/10.1016/j.proeng.2012.01.868openalex-snapshot
Fuzzy Clustering and Deformable Model for Tumor Segmentation on MRI Brain Image: A Combined Approach
inferredcites →https://doi.org/10.1016/s0730-725x(96)00386-4openalex-snapshot
Monitoring brain tumor response to therapy using MRI segmentation
inferredcites →https://doi.org/10.1016/s0730-725x(97)00302-0openalex-snapshot
MRI Measurement of Brain Tumor Response: Comparison of Visual Metric and Automatic Segmentation
inferredcites →https://doi.org/10.1017/cbo9780511801389openalex-snapshot
An Introduction to Support Vector Machines and Other Kernel-based Learning Methods
inferredcites →https://doi.org/10.1023/a:1009715923555openalex-snapshot
A Tutorial on Support Vector Machines for Pattern Recognition
inferredcites →https://doi.org/10.1097/rli.0b013e318277f078openalex-snapshot
Computer-Aided Detection of Metastatic Brain Tumors Using Magnetic Resonance Black-Blood Imaging
inferredcites →https://doi.org/10.1109/acssc.2006.355112openalex-snapshot
Brain Tumor Detection in MRI: Technique and Statistical Validation
inferredcites →https://doi.org/10.1109/iccci.2012.6158908openalex-snapshot
Artificial neural networks design for classification of brain tumour
inferredcites →https://doi.org/10.1109/iccic.2010.5705890openalex-snapshot
Diagnose brain tumor through MRI using image processing clustering algorithms such as Fuzzy C Means along with intelligent optimization techniques
inferredcites →https://doi.org/10.1109/iccicct.2014.6993142openalex-snapshot
An efficient brain tumor detection by integrating modified texture based region growing and cellular automata edge detection
inferredcites →https://doi.org/10.1109/iccnit.2011.6020885openalex-snapshot
Computer aided system for brain tumor detection and segmentation
inferredcites →https://doi.org/10.1109/icsccn.2011.6024532openalex-snapshot
Automatic detection of brain tumor based on magnetic resonance image using CAD System with watershed segmentation
inferredcites →https://doi.org/10.1109/isbi.2012.6235613openalex-snapshot
Automatic brain tumor detection in Magnetic Resonance Images
inferredcites →https://doi.org/10.1109/wccct.2014.26openalex-snapshot
Performance Analysis of Fuzzy C Means Algorithm in Automated Detection of Brain Tumor
inferredcites →https://doi.org/10.1111/jon19999285openalex-snapshot
Feature Extraction for MRI Segmentation
inferredcites →https://doi.org/10.1148/radiology.212.3.r99se22811openalex-snapshot
Brain Tumor Volume Measurement: Comparison of Manual and Semiautomated Methods
inferredcites →https://doi.org/10.7763/ijet.2011.v3.280openalex-snapshot
Computer Aided Detection of Brain Tumor in Magnetic Resonance Images

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.2139/ssrn.3372921openalex-extracted
Brain Tumor Classification Using GLCM Features and Neural Network
inferred← citeshttps://doi.org/10.1007/978-3-031-92854-3_7openalex-snapshot
Brain Tumor Detection and Classification Using Machine Learning Approach
inferred← citeshttps://doi.org/10.1007/978-3-032-25949-3_6openalex-snapshot
Enhancing MRI-Based Brain Tumor Detection with ResNet101: A Deep Learning Architecture
inferred← citeshttps://doi.org/10.1007/978-981-15-0751-9_52openalex-snapshot
Classification of Brain MRI Using Deep Learning Techniques
inferred← citeshttps://doi.org/10.1007/978-981-15-8534-0_21openalex-snapshot
Digital Image Analysis Is a Silver Bullet to COVID-19 Pandemic
inferred← citeshttps://doi.org/10.1007/978-981-16-2911-2_10openalex-snapshot
Target Detection from Brain MRI and Its Classification
inferred← citeshttps://doi.org/10.1007/978-981-16-3368-3_19openalex-snapshot
Review of Segmentation and Classification Techniques in Computer-Aided Detection of Brain Tumor from MRI
inferred← citeshttps://doi.org/10.1007/978-981-16-3690-5_24openalex-snapshot
An Optimal Region Growing Segmentation Algorithm with Decision Tree Tumor Classifier
inferred← citeshttps://doi.org/10.1007/978-981-16-5348-3_41openalex-snapshot
Classification of Brain Tumor Images Using Enhanced Deep Learning-Based Methodologies
inferred← citeshttps://doi.org/10.1007/978-981-16-8515-6_55openalex-snapshot
Computer Aided System for Automatic Detection of Brain Tumor
inferred← citeshttps://doi.org/10.1007/978-981-19-0976-4_43openalex-snapshot
A Research Study on Brain Tumor Detection Techniques
inferred← citeshttps://doi.org/10.1007/978-981-97-5791-6_9openalex-snapshot
Brain Tumor Detection Using NMDTF-IP Image Segmentation Approach
inferred← citeshttps://doi.org/10.1007/978-981-99-4626-6_30openalex-snapshot
Brain Tumor Detection and Classification
inferred← citeshttps://doi.org/10.1007/978-981-99-8031-4_4openalex-snapshot
Vitiligo Detection Using Machine Learning Algorithms
inferred← citeshttps://doi.org/10.1007/s10916-019-1368-4openalex-snapshot
Brain Tumor Detection and Segmentation by Intensity Adjustment
inferred← citeshttps://doi.org/10.1007/s11042-022-12162-1openalex-snapshot
RETRACTED ARTICLE: Review of brain tumor detection from MRI images with hybrid approaches
inferred← citeshttps://doi.org/10.1007/s11042-023-14857-5openalex-snapshot
Recent advancement in learning methodology for segmenting brain tumor from magnetic resonance imaging -a review
inferred← citeshttps://doi.org/10.1007/s12652-020-02444-7openalex-snapshot
RETRACTED ARTICLE: MRI brain tumor detection using optimal possibilistic fuzzy C-means clustering algorithm and adaptive k-nearest neighbor classifier
inferred← citeshttps://doi.org/10.1016/b978-0-443-19073-5.00001-xopenalex-snapshot
A hybrid ResNet-18-UNet model for MRI brain tumor segmentation
inferred← citeshttps://doi.org/10.1016/j.bbe.2020.01.006openalex-snapshot
Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach
inferred← citeshttps://doi.org/10.1016/j.bspc.2022.104411openalex-snapshot
A dual tri-path CNN system for brain tumor segmentation
inferred← citeshttps://doi.org/10.1016/j.bspc.2022.104424openalex-snapshot
Improving brain tumor classification performance with an effective approach based on new deep learning model named 3ACL from 3D MRI data
inferred← citeshttps://doi.org/10.1016/j.cmpb.2021.106188openalex-snapshot
Automatic brain tumour diagnostic method based on a back propagation neural network and an extended set-membership filter
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108412openalex-snapshot
Brain tumor detection with integrating traditional and computational intelligence approaches across diverse imaging modalities - Challenges and future directions
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110242openalex-snapshot
Real-time brain tumour diagnoses using a novel lightweight deep learning model
inferred← citeshttps://doi.org/10.1016/j.imu.2020.100498openalex-snapshot
Automatic segmentation and quantification of hair follicle orientation
inferred← citeshttps://doi.org/10.1016/j.matpr.2022.06.016openalex-snapshot
A proposed brain tumor detection algorithm using Multi wavelet Transform (MWT)
inferred← citeshttps://doi.org/10.1016/j.mri.2019.05.043openalex-snapshot
A review on brain tumor segmentation of MRI images
inferred← citeshttps://doi.org/10.1038/s41598-025-13155-4openalex-snapshot
A successive framework for brain tumor interpretation using Yolo variants
inferred← citeshttps://doi.org/10.1109/access.2021.3100549openalex-snapshot
Pattern Descriptors Orientation and MAP Firefly Algorithm Based Brain Pathology Classification Using Hybridized Machine Learning Algorithm
inferred← citeshttps://doi.org/10.1109/access.2024.3359418openalex-snapshot
Automated Brain Tumor Segmentation and Classification in MRI Using YOLO-Based Deep Learning
inferred← citeshttps://doi.org/10.1109/ecce57851.2023.10101618openalex-snapshot
Brain Tumor Detection and Classification by SVM Algorithm and Performance Analysis Through CNN Approach
inferred← citeshttps://doi.org/10.1109/icaecc.2018.8479530openalex-snapshot
Texture Analysis of Brain Mr Images
inferred← citeshttps://doi.org/10.1109/iccakm54721.2022.9990517openalex-snapshot
Analysis of Coloboma Defected Eyes using Automated Pre-trained CNN Models
inferred← citeshttps://doi.org/10.1109/iccmc56507.2023.10083833openalex-snapshot
Brain Tumor Detection using Mask RCNN
inferred← citeshttps://doi.org/10.1109/icecaa58104.2023.10212125openalex-snapshot
Wave Deep Neural Network for Multi-Class MRI Brain Image Classification
inferred← citeshttps://doi.org/10.1109/iciccs51141.2021.9432346openalex-snapshot
Brain MRI Segmentation and Tumor Detection: Challenges, Techniques and Applications
inferred← citeshttps://doi.org/10.1109/icvee57061.2022.9930354openalex-snapshot
Brain Tumor Classification Using Deep Neural Network Based on MRI Images
inferred← citeshttps://doi.org/10.1109/igeht.2017.8094064openalex-snapshot
A survey on image processing techniques for brain tumor detection using magnetic resonance imaging
inferred← citeshttps://doi.org/10.1109/jcsse58229.2023.10202020openalex-snapshot
Enhancing Brain Tumor Detection with Gradient-Weighted Class Activation Mapping and Deep Learning Techniques
inferred← citeshttps://doi.org/10.1109/sceecs54111.2022.9741022openalex-snapshot
Retracted: Analysis of Brain Tumor using Pre-trained CNN Models and Machine Learning Techniques
inferred← citeshttps://doi.org/10.1109/tqcebt54229.2022.10041564openalex-snapshot
Detection of Brain Tumor in Medical Images Based on Feature Extraction by HOG and Machine Learning Algorithms
inferred← citeshttps://doi.org/10.1109/wids-psu57071.2023.00028openalex-snapshot
Automated Hybrid Model for Detection and Classification of Brain Tumor
inferred← citeshttps://doi.org/10.11591/ijeecs.v14.i1.pp478-489openalex-snapshot
Hybrid enhanced ICA & KSVM based brain tumor image segmentation
inferred← citeshttps://doi.org/10.1364/ao.57.000b25openalex-snapshot
Hybrid gray wolf optimizer–artificial neural network classification approach for magnetic resonance brain images
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A Hybrid Enhanced Independent Component Analysis Approach for Segmentation of Brain Magnetic Resonance Image
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Brain abnormality detection using template matching
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A Novel Approach to Classify Brain Tumor with an Effective Transfer Learning based Deep Learning Model
inferred← citeshttps://doi.org/10.18280/ts.380406openalex-snapshot
The Detection of Brain Tumors Using Chan-Vese Active Contour Without Edges Method in Magnetic Resonance (MR) Images
inferred← citeshttps://doi.org/10.21172/1.91.15openalex-snapshot
Brain tumor detection in magnetic resonance images
inferred← citeshttps://doi.org/10.2139/ssrn.5089153openalex-snapshot
Optimized Feature Extraction Technique by Combining Improved Zernike Features and Hybrid Wavelet Walsh Transform
inferred← citeshttps://doi.org/10.21742/ijiphm.2015.2.2.02openalex-snapshot
Detection and Classification Brain Tumor of Magnetic Resonance Imaging Using 2D Gabor Wavelet Transform and Artificial Neural Network: A Review
inferred← citeshttps://doi.org/10.32604/csse.2023.028542openalex-snapshot
Nonlinear Teager-Kaiser Infomax Boost Clustering Algorithm for Brain Tumor Detection Technique
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IMAGE SEGMENTATION OF MRI IMAGE FOR BRAIN TUMOR DETECTION
inferred← citeshttps://doi.org/10.3390/axioms11010015openalex-snapshot
Automated Detection and Classification of Meningioma Tumor from MR Images Using Sea Lion Optimization and Deep Learning Models
inferred← citeshttps://doi.org/10.3390/electronics11213457openalex-snapshot
Brain Tumor Classification and Detection Using Hybrid Deep Tumor Network
inferred← citeshttps://doi.org/10.35940/ijrte.a1239.058119openalex-snapshot
A Unique Six Sigma Based Segmentation Technique for Brain Tumor Detection and Classification using Hybrid CNN-SVM Model
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Analysis of Hybrid Feature Optimization Techniques Based on the Classification Accuracy of Brain Tumor Regions Using Machine Learning and Further Evaluation Based on the Institute Test Data
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A SYSTEMATIC REVIEW ON BRAIN MRI CLASSIFICATION
Editorial review for independent work. ICSAC — the Institute for Complexity Science and Advanced Computing — runs a structured editorial panel for researchers publishing outside journals, and publishes on CiteStamp rails. Submission is free and goes through ORCID: icsacinstitute.org/submit.