Brain Tumor Segmentation Using Fuzzy C-Means and Tumor Grade Classification Using SVM

Sajja, V Ramakrishna; Kalluri, Hemantha Kumar

doi:10.1007/978-981-15-2407-3_24

Cited by 4 publications

(2 citation statements)

References 2 publications

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“…A key study by Ramakrishna Sajja and Kalluri [11] was to segment brain tumors in MRI using a combination of fuzzy c‐means (FCM) clustering and support vector machine (SVM) classification. The FCM algorithm was used to segment the brain tumors, while the SVM classifier was used to classify the tumors as benign or malignant.…”

Section: Related Workmentioning

confidence: 99%

Brain Tumor Segmentation Based on α‐Expansion Graph Cut

Soloh,

Alabboud,

Shahin

et al. 2024

Int J Imaging Syst Tech

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In recent years, there has been an increased interest in using image processing, computer vision, and machine learning in biological and medical imaging research. One area of this interest is the diagnosis of brain tumors, which is considered a difficult and time‐consuming task traditionally performed manually. In this study, we present a method for tumor detection from magnetic resonance images (MRI) using a well‐known graph‐based algorithm, the Boykov–Kolmogorov algorithm, and the α‐expansion method. This approach involves pre‐processing the MRIs, representing the image positions as nodes, and calculations of the weights between edges as differences in intensity. The problem is formulated as an energy minimization problem and is solved by finding the 0,1 score for the image. Post‐processing is also performed to enhance the overall segmentation. The proposed method is easy to implement and shows high accuracy, precision, and efficiency in the results. We believe that this approach will bring significant benefits to scientists and healthcare researchers in qualitative research and can be applied to various imaging modalities for future research.

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Section: Related Workmentioning

confidence: 99%

Brain Tumor Segmentation Based on α‐Expansion Graph Cut

Soloh,

Alabboud,

Shahin

et al. 2024

Int J Imaging Syst Tech

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show abstract

“…The concept of dual path residual CNN was used in this manner, and the model achieved a classification accuracy of 84.9 percent [20]. Similarly, Sajja et al used an integrated architecture with fuzzy C-means clustering and a support vector machine (SVM) to produce a classification error rate of 5.2 and a 94.8 percent accuracy score [21]. The critical feature was selected using principal component analysis (PCA) and Gray-level co-occurrence matrix (GLCM) to detect the presence of brain tumours and their classification into malignant and benign categories using support vector machine (SVM) [22].…”

Section: Related Workmentioning

confidence: 99%

Classification of brain tumor of magnetic resonance medical images using convolutional neural network approach

Verma¹,

Arora²

2022

ijhs

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The impact tumours in the brain in medical field cannot be ignored and may lead to a short life in their highest grade. Thus, conduction of proper diagnosis that too in its early stage to improve the quality of life of patients is a necessity. Normally, several image processing techniques including Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) is a type of imaging that uses a (MRI) are be ingutilized to localize and calculate the size and tumor in a brain. But has limited performance for accurate quantitative measurements that too in a small number of sample images. In this project, a simple yet robust classification using Convolutional Neural Networks (CNN) for for brain cancer is proposed. The investigational outcomes with low complication is anticipated and potentially competes the relevant state of arts methods.

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Classification of Brain Tumor of Magnetic Resonance Images Using Convolutional Neural Network Approach

Sinha¹,

Verma²

2022

Computational Intelligence and Data Analytics

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Brain Tumor Segmentation Using Fuzzy C-Means and Tumor Grade Classification Using SVM

Cited by 4 publications

References 2 publications

Brain Tumor Segmentation Based on α‐Expansion Graph Cut

Brain Tumor Segmentation Based on α‐Expansion Graph Cut

Classification of brain tumor of magnetic resonance medical images using convolutional neural network approach

Classification of Brain Tumor of Magnetic Resonance Images Using Convolutional Neural Network Approach

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