Robust Classification of Primary Brain Tumor in MRI Images based on Multi Model Textures Features and Kernel Based SVM

Prabin, A.; Veerappan, J.

doi:10.13005/bpj/805

Cited by 6 publications

(3 citation statements)

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“…1 . MRI 25 provides valuable diagnostic information to characterize brain tumors 2 , but challenges lie in the ability to classify different tumor types. In contrast to pathology, MRIs cannot make definitive diagnoses of brain tumor subtypes.…”

Section: Introductionmentioning

confidence: 99%

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

Hussain

Malibari

Alamgeer

et al. 2022

Sci Rep

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Accurate classification of brain tumor subtypes is important for prognosis and treatment. Researchers are developing tools based on static and dynamic feature extraction and applying machine learning and deep learning. However, static feature requires further analysis to compute the relevance, strength, and types of association. Recently Bayesian inference approach gains attraction for deeper analysis of static (hand-crafted) features to unfold hidden dynamics and relationships among features. We computed the gray level co-occurrence (GLCM) features from brain tumor meningioma and pituitary MRIs and then ranked based on entropy methods. The highly ranked Energy feature was chosen as our target variable for further empirical analysis of dynamic profiling and optimization to unfold the nonlinear intrinsic dynamics of GLCM features extracted from brain MRIs. The proposed method further unfolds the dynamics and to detailed analysis of computed features based on GLCM features for better understanding of the hidden dynamics for proper diagnosis and prognosis of tumor types leading to brain stroke.

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Section: Introductionmentioning

confidence: 99%

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

Hussain

Malibari

Alamgeer

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…Accurate brain tumor classification is crucial for optimal patient care, including monitoring, treatment, prognosis, and treatment planning 20 . MRI 21 provides valuable diagnostic information for brain tumors 22 . However, MRI cannot definitively diagnose brain tumor subtypes, and accurate classification using MRI remains a challenge.…”

Section: Related Workmentioning

confidence: 99%

Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach

Shim,

Hussain,

Aziz

et al. 2024

Int J Imaging Syst Tech

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Accurate brain tumor classification is crucial for enhancing the diagnosis, prognosis, and treatment of glioblastoma patients. We employed the ResNet101 deep learning method with transfer learning to analyze the 2021 Radiological Society of North America (RSNA) Brain Tumor challenge dataset. This dataset comprises four structural magnetic resonance imaging (MRI) sequences: fluid‐attenuated inversion‐recovery (FLAIR), T1‐weighted pre‐contrast (T1w), T1‐weighted post‐contrast (T1Gd), and T2‐weighted (T2). We assessed the model's performance using standard evaluation metrics. The highest performance to detect MGMT methylation status for patients suffering glioblastoma was an accuracy (85.48%), sensitivity (80.64%), specificity (90.32%). Whereas classification performance with no tumor was yielded with accuracy (85.48%), sensitivity (90.32%), specificity (80.64%). The radiomic features (74) computed with ensembled Bagged Tree and relief feature selection method (30/74) improved the validation accuracy of 84.3% and AUC of 0.9038 to detect. O6‐methylguanine‐DNA methyltransferase (MGMT) promoter methylation status in glioblastoma patients holds promise for optimizing treatment planning and prognosis. By understanding MGMT methylation status, clinicians can make informed decisions about treatment strategies, potentially leading to improved clinical outcomes.

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“…Accurate classification of brain tumor subtypes is important for prognosis, treatment, monitoring, and planning 1 . MRI 2 provides valuable diagnostic information to characterize brain tumors, 3 but challenges lie in the ability to classify different tumor types. In contrast to pathology, MRIs cannot make definitive diagnoses of brain tumor subtypes.…”

Section: Introductionmentioning

confidence: 99%

Detecting brain tumors using deep learning convolutional neural network with transfer learning approach

Anjum

Hussain

Ali

et al. 2021

Int J Imaging Syst Tech

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Accurate classification of brain tumor subtypes is important for prognosis and treatment. In this study, we optimized and applied non-deep learning methods based on hand-crafted features and deep learning methods based on transfer learning using softmax as classification and KNN and SVM as classification for features extracted from deep features of ResNet101. For non-deep learning techniques, we extracted multimodal features as input to machine learning classifiers. For convolutional neural networks, we optimized and applied GoogleNet and ResNet101with transfer learning approach. The performance was evaluated in terms of sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), false positive rate (FPR), total accuracy (TA), and area under the receiver operating curve (AUC) using Jack-knife 10-fold cross validation (CV) for the testing and validation of the dataset. For two-class classification, entropy features using SVM Gaussian yielded the highest performance with 93.84% TA and 0.9874 AUC, and GoogleNet yielded 99.33% TA. For Multiclass classification, the highest performance to detect pituitary tumor yielded 95.65% accuracy and 0.95 AUC using ResNet101 with transfer learning. Deep features from ResNet101 using KNN improved detection of pituitary tumor (98.80% accuracy, 0.99 AUC), glioma (93.47% accuracy, 0.93 AUC), and meningioma (93.36% accuracy, 0.89 AUC). The deep features ResNet101-SVM to detect pituitary tumor yielded performance (98.

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Robust Classification of Primary Brain Tumor in MRI Images based on Multi Model Textures Features and Kernel Based SVM

Cited by 6 publications

References 0 publications

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

Bayesian dynamic profiling and optimization of important ranked energy from gray level co-occurrence (GLCM) features for empirical analysis of brain MRI

Deep learning convolutional neural network ResNet101 and radiomic features accurately analyzes mpMRI imaging to predict MGMT promoter methylation status with transfer learning approach

Detecting brain tumors using deep learning convolutional neural network with transfer learning approach

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