Automatic classification of MR brain tumor images using KNN, ANN, SVM and CNN

Rajini, N. Hema

doi:10.21744/irjeis.v3n1.895

Cited by 6 publications

(6 citation statements)

References 12 publications

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“…The ion-magnetic cation substitution Ce 4+ is located at the Ba atomic site, thus causing an increase in the total number of magnetic moments thought to originate from the presence of magnetic doping ions. It is shown based on Table 2, that the magnetic saturation of samples doped with La 3+ and Ce 4+ ions is greater than that of pure barium hexaferrite (Gupta & Khan, 2021;Rajini, 2017).…”

Section: Resultsmentioning

confidence: 96%

Study of the effect of lanthanum and cerium doping combination on magnetic properties of M-type hexaferrite oxide permanent magnets

Adnyana

Suarbawa²

2022

ijcms

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A study has been carried out on the effect of combination of Lanthanum and Cerium doping on the magnetic properties of M-Type hexaferrite permanent magnet oxide. The research was conducted using the wet mechanical milling method. The research materials used were Fe2O3, BaCO3, La2O3, and CeO2, all of which were of high purity 99.9%. This research also uses technical materials such as Ethanol and Nitrogen. Stoichiometric calculations were carried out to calculate the composition of the mixture of each ingredient. The combination of Lanthanum and Cerium substitution was varied with the composition of the doping ion concentration (x = 0 – 0.5 and y = 0 – 0.1). Permagraph characterization was carried out to determine the magnetic properties of each test sample. Results based on the permagraph characterization, the properties of M-type hexaferrite permanent magnets increased with the increase in the doping ion concentration in the sample. The presence of doping ions that have a magnetic moment is strongly suspected to contribute to the interaction of the type and the total moment in the M- type hexaferrite structure.

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Section: Resultsmentioning

confidence: 96%

Study of the effect of lanthanum and cerium doping combination on magnetic properties of M-type hexaferrite oxide permanent magnets

Adnyana

Suarbawa²

2022

ijcms

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“…Then they use watershed transform to obtain the segmentation results. Rajini N et al [61] proposed a method combining threshold segmentation and watershed. First, the image was segmented by threshold method, and then the segmented image was segmented by watershed algorithm.…”

Section: Segmentation Methods Based On Regionmentioning

confidence: 99%

Overview of Multi-Modal Brain Tumor MR Image Segmentation

Zhang

Yang³

et al. 2021

Healthcare

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The precise segmentation of brain tumor images is a vital step towards accurate diagnosis and effective treatment of brain tumors. Magnetic Resonance Imaging (MRI) can generate brain images without tissue damage or skull artifacts, providing important discriminant information for clinicians in the study of brain tumors and other brain diseases. In this paper, we survey the field of brain tumor MRI images segmentation. Firstly, we present the commonly used databases. Then, we summarize multi-modal brain tumor MRI image segmentation methods, which are divided into three categories: conventional segmentation methods, segmentation methods based on classical machine learning methods, and segmentation methods based on deep learning methods. The principles, structures, advantages and disadvantages of typical algorithms in each method are summarized. Finally, we analyze the challenges, and suggest a prospect for future development trends.

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“…An ANN and its variant-based classification were described in [17][18][19][20], where the GLCM-based texture features along with some intensity-based features were used [17]. The dimensionality of the feature vector was further reduced using PCA.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Assisted Methodology for Multiclass Classification of Malignant Brain Tumors

Vidyarthi

Agarwal²,

Gupta

et al. 2022

IEEE Access

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Analysis of malignant and non-malignant brain tumors is done using a computer-aided diagnosis system by practitioners worldwide. Radiologists refer computer-assisted techniques to draw conclusions using image modalities and inferences. Pedagogically, various machine learning approaches have been used, which usually focus on the classification of imaging modality into two categories, either normal and abnormal images or differentiating between benign and malignant tumors. Still, the work requirement is to classify these multi-class malignant tumors into their specific class with better precision. The proposed work focuses on distinguishing between the types of high-grade malignant brain tumors. This study is performed on real-life malignant brain tumor datasets having five classes. The proposed methodology uses the vast feature set from six domains to capture most of the hidden information in the extracted region of interest. Later, relevant features are extracted from the feature set pool using a new proposed feature selection algorithm named the Cumulative Variance method (CVM). Next, the selected features are used for model training and testing using K-Nearest Neighbour (KNN), multi-class Support Vector Machine (mSVM), and Neural Network (NN) for predicting multi-class classification accuracy. The experiments are performed using the proposed feature selection algorithm with three classifiers. The mean average classification accuracy achieved by using the proposed approach is88.43% (KNN), 92.5% (mSVM), and 95.86% (NN), respectively. The comparative analysis of the proposed approach with other existing algorithms like ICA, and GA suggest that the proposed approach gains an increase of accuracy around 2% (KNN), 3% (SVM), and 4% (NN).The experimentation results concluded that the proposed approach is found better with NN classifier with an accuracy of 95.86% using diversified features.

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Automatic classification of MR brain tumor images using KNN, ANN, SVM and CNN

Cited by 6 publications

References 12 publications

Study of the effect of lanthanum and cerium doping combination on magnetic properties of M-type hexaferrite oxide permanent magnets

Study of the effect of lanthanum and cerium doping combination on magnetic properties of M-type hexaferrite oxide permanent magnets

Overview of Multi-Modal Brain Tumor MR Image Segmentation

Machine Learning Assisted Methodology for Multiclass Classification of Malignant Brain Tumors

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