Malignant and Benign Brain Tumor Segmentation and Classification Using SVM with Weighted Kernel Width

Rezaei, Kimia; Agahi, Hamed

doi:10.5121/sipij.2017.8203

Cited by 9 publications

(1 citation statement)

References 15 publications

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“…Rezaei and H. Agahi also classified brain tumors and obtained an SVM accuracy of 76% [39]. Our proposed model provides better accuracy results for brain tumor classification tasks from the three previous studies utilized as a comparison.…”

Section: Resultsmentioning

confidence: 79%

Parameter Optimization of Support Vector Machine using River Formation Dynamic on Brain Tumor Classification

Cahya Kemila,

Fawwaz Al Maki

2023

j.electron.electromedical.eng.med.inform

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Brain tumor is a condition that can interfece with brain function due to abnormal cell growth in the brain. MRI is used as a diagnostic tool when a patient has a brain tumor. The images obtained through MRI will be analyzed by the doctor to determine the type of tumor. Therefore, it is necessary to have a system that can classify tumor types based on MRI images. The image will be extracted using the HOG method, then classified using SVM. Certain measures can be taken to improve SVM performance, such as optimizing its parameters. This research develops a system that uses a novel combination, the SVM with the River Formation Dynamic (RFD) algorithm. RFD is being used to optimize parameters of SVM (C and gamma). The basic idea of RFD is to imitate the movement of water droplets flowing from high to low areas. This research compares the accuracy produced by SVM with the accuracy produced by SVM-RFD. The result is that SVM-RFD provides the better accuracy than only using SVM. The accuracy result by SVM on the MRI dataset is 74.37%. When we compared it with SVM-RFD, the accuracy increased by 13.19% to 87.56%. Further work will be carried out on the implementation of RFD on other SVM parameters to find other parameter combinations that can improve the accuracy of SVM.

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Section: Resultsmentioning

confidence: 79%

Parameter Optimization of Support Vector Machine using River Formation Dynamic on Brain Tumor Classification

Cahya Kemila,

Fawwaz Al Maki

2023

j.electron.electromedical.eng.med.inform

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Automation of Brain Tumor Segmentation Using Deep Learning

Verma

2023

Advanced Technologies and Societal Change

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MRI Brain Images Classification: A Multi-Level Threshold Based Region Optimization Technique

Kanmani

Marikkannu

2018

J Med Syst

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Medical image processing is the most challenging and emerging field nowadays. Magnetic Resonance Images (MRI) act as the source for the development of classification system. The extraction, identification and segmentation of infected region from Magnetic Resonance (MR) brain image is significant concern but a dreary and time-consuming task performed by radiologists or clinical experts, and the final classification accuracy depends on their experience only. To overcome these limitations, it is necessary to use computer-aided techniques. To improve the efficiency of classification accuracy and reduce the recognition complexity involves in the medical image segmentation process, we have proposed Threshold Based Region Optimization (TBRO) based brain tumor segmentation. The experimental results of proposed technique have been evaluated and validated for classification performance on magnetic resonance brain images, based on accuracy, sensitivity, and specificity. The experimental results achieved 96.57% accuracy, 94.6% specificity, and 97.76% sensitivity, shows the improvement in classifying normal and abnormal tissues among given images. Detection, extraction and classification of tumor from MRI scan images of the brain is done by using MATLAB software.

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Malignant and Benign Brain Tumor Segmentation and Classification Using SVM with Weighted Kernel Width

Cited by 9 publications

References 15 publications

Parameter Optimization of Support Vector Machine using River Formation Dynamic on Brain Tumor Classification

Parameter Optimization of Support Vector Machine using River Formation Dynamic on Brain Tumor Classification

Automation of Brain Tumor Segmentation Using Deep Learning

MRI Brain Images Classification: A Multi-Level Threshold Based Region Optimization Technique

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