Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification

Susanto, Ajib; Sari, Christy Atika; Rahmalan, Hidayah; Doheir, Mohamed

doi:10.12928/telkomnika.v21i3.24928

Cited by 5 publications

(1 citation statement)

References 30 publications

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“…In that study, two algorithms, SVM and kNN, were used and gave an accuracy of 96.8% and 91.75%, respectively. Susanto et al [ 16 ] also used the traditional feature extraction method, GLCM and DWT, to extract 16 features and classified by SVM algorithm with an accuracy of 98.65%. Finally, in the most recent study, Aamir et al [ 17 ] used a new feature extraction method called multiple deep neural networks, also using SVM classification algorithm, and achieved an accuracy of 98.98%.…”

Section: Related Workmentioning

confidence: 99%

High-Performance Method for Brain Tumor Feature Extraction in MRI Using Complex Network

Han Trong,

Nguyen Van,

Vu Dang

2023

Applied Bionics and Biomechanics

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Objective. To localize and distinguish between benign and malignant tumors on MRI. Method. This work proposes a high-performance method for brain tumor feature extraction using a combination of complex network and U-Net architecture. And then, the common machine-learning algorithms are used to discriminate between benign and malignant tumors. Experiments and Results. The dataset of brain MRI of a total of 230 brain tumor patients in which 77 high-grade glioma patients and 153 low-grade glioma patients were processed. The results of classifying benign and malignant tumors achieved an accuracy of 99.84%. Conclusion. The high accuracy of experiment results demonstrates that the use of the complex network and U-Net architecture can significantly improve the accuracy of brain tumor classification. This method could potentially be useful for clinicians in aiding diagnosis and treatment planning for brain tumor patients.

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Section: Related Workmentioning

confidence: 99%

High-Performance Method for Brain Tumor Feature Extraction in MRI Using Complex Network

Han Trong,

Nguyen Van,

Vu Dang

2023

Applied Bionics and Biomechanics

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Optimal extreme learning machine for diagnosing brain tumor based on modified sailfish optimizer

Ali Amin,

Alqudah,

Ateeq Almutairi

et al. 2024

Heliyon

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Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models

Akter,

Simul Hasan Talukder,

Mondal

et al. 2024

Sci Rep

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Brain tumors are among the most fatal and devastating diseases, and they often result in a significant reduction in life expectancy. The devising of treatment plans that can extend the lives of affected individuals hinges on an accurate diagnosis of these tumors. Identifying and analyzing large volumes of magnetic resonance imaging (MRI) data manually proves to be both challenging and time-consuming. As a result, there exists a pressing need for a reliable machine-learning approach to accurately diagnose brain tumors, and numerous methods have already been proposed over the last decade. In this paper, a novel, comprehensive approach is proposed for identifying and classifying a given MR brain image as abnormal. Three common brain diseases, namely glioma, meningioma, and pituitary tumor, are chosen as abnormal brains, and the Figshare MRI brain image dataset was collected from the Kaggle and IEEE websites. The proposed method is initiated by employing 1st-order statistics, 2nd-order statistics, and higher-order transformed (DWT) feature extraction to extract features from images. Then missing data is addressed and handled using KNNImputer, followed by the application of the ExtratreesClassifier and PCA feature selection methods to identify the most relevant features and reduce the dimensions of these features. Subsequently, the reduced features are submitted to seven machine learning models, namely RF, GB, CB, SVM, LGBM, DT, and LR. The strategy of k-fold cross-validation is utilized to enhance the performance of those models. Finally, the models are evaluated using XAI approaches, which ensure transparent decision-making processes and provide insights into the model’s predictions. Remarkably, our approach achieves the highest accuracy, precision, recall, F1 score, MCC, Kappa, AUC-ROC, and R2, as well as the lowest loss, among the seven models evaluated, proving its effectiveness and applicability in multiple analytic applications relying on publicly available datasets.

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Support vector machine based discrete wavelet transform for magnetic resonance imaging brain tumor classification

Cited by 5 publications

References 30 publications

High-Performance Method for Brain Tumor Feature Extraction in MRI Using Complex Network

High-Performance Method for Brain Tumor Feature Extraction in MRI Using Complex Network

Optimal extreme learning machine for diagnosing brain tumor based on modified sailfish optimizer

Brain tumor classification utilizing pixel distribution and spatial dependencies higher-order statistical measurements through explainable ML models

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