Brain Tumor Segmentation and Classification using Spatial Fuzzy C mean and Quadratic Support Vector Machine

Ayon, Ragib Shahariar; Mou, Jannatul Robaiat; Majed, Sharafat Hossain; Rifat, Rathyatul

doi:10.1109/icecte48615.2019.9303511

Cited by 1 publication

(1 citation statement)

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“…The results show that the quadratic SVM has an effective performance with accuracy of 93.6% Ragib et. al [17] this study proposes an improved approach called Spatial Fuzzy C-Means (SFCM) for brain tumor segmentation. Additionally article presents the Quadratic Support Vector Machine (QSVM) for the categorization of tumour types, providing an effective and robust classification model for differentiating tumor types based on the segmented regions.…”

Section: Literature Reviewmentioning

confidence: 99%

Brain Tumor Classification, Segmentation, and Detection using Deep Learning - A Review

Mrs. Deepali K. Jadhav

2023

IJRITCC

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V.Vapnik in 1965 proposed Vector methods. Kimeldorf presented a technique for creating kernel space based on support vectors in 1971. Support Vector Machine (SVM) techniques were initially presented in the 1990s by V. Vapnik in the field of statistical learning. Since then, pattern recognition, natural language processing, image processing and other areas have seen extensive use of SVM. By converting non-linear sample space into linear space via a kernel approach, the algorithm's complexity is reduced. Image classification is a well-known issue in image processing. Predicting the input image categories using the features is the main objective of image classification. There are several different classifiers, including Artificial Neural Networks, Support Vector Machines, and Random Forests, Decision Forests, k-NNs (k Nearest Neighbors), and Adaptive Boost. SVM is one of the best techniques for categorizing any image or pattern. A common non-invasive technique used in the medical sector for the analysis, diagnosis, treatment of brain tissues is magnetic resonance imaging. When a brain tumor is discovered early, the patient's life can be saved by receiving the appropriate care. It becomes difficult to accurately identify tumors in the MRI slices, which requires fussy work..

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Section: Literature Reviewmentioning

confidence: 99%

Brain Tumor Classification, Segmentation, and Detection using Deep Learning - A Review

Mrs. Deepali K. Jadhav

2023

IJRITCC

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Brain Tumor Segmentation and Classification using Spatial Fuzzy C mean and Quadratic Support Vector Machine

Cited by 1 publication

References 9 publications

Brain Tumor Classification, Segmentation, and Detection using Deep Learning - A Review

Brain Tumor Classification, Segmentation, and Detection using Deep Learning - A Review

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