Comparative Study for Brain Tumor Classification on MR/CT Images

Vidyarthi, Ankit; Mittal, Namita

doi:10.1007/978-81-322-1771-8_77

Cited by 15 publications

(13 citation statements)

References 16 publications

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“…In [8], the author gives the brief comparative study about the classification of the brain tumors which is done in past by various researchers of different domains. In this paper the work over classification of brain tumors is presented in tabular form which mention the dataset used by researchers for experimental purpose with type of feature extraction mechanism used with corresponding feature selection methods.…”

Section: Related Workmentioning

confidence: 99%

“…The performance of a classifier depends on the number of present training samples. Here two supervised learning classifier K-Nearest Neighbor (KNN) [8,19] and Back Propagation Neural Network (BPNN) [20] are used for the classification of input feature vector.…”

Section: Classificationmentioning

confidence: 99%

“…Several methods are used for Feature extraction, such as Gray Level co-occurrence Matrix(GLCM) [1][2][3][4][5][6][7][8][9][10], Dominant Gray Level Run Length Matrix method(DGLRLM) [13,18], Spatial Gray Level Dependence Matrix method (SGLDM) [13,18], Discrete-Wavelet transform(DWT) [17], Gabor Filters [13] and Local Binary Patterns (LBP) [13][14][15][16] and a considerable number of quantitative features namely Mean, Variance, Contrast, Correlation, Entropy, Energy, Homogeneity, Kurtosis, Inverse Difference Moment, Sum Average, Sum Entropy, Difference Entropy, Inertia [1,2] can be extracted from images using different methodologies in order to characterize different properties, and further can be used as the input of classifier to classify image pixels.…”

Section: Introductionmentioning

confidence: 99%

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CLOM: Counting label occurrence matrix for feature extraction in MR images

Nagpal

Vidyarthi

Mittal

2015

2015 International Conference on Signal Processing and Communication (ICSC)

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The general phenomenon for Image Classification is based on the Feature extraction mechanism. In every domain of image analysis, the classification accuracy is dependent on how better the feature set is generated which helps the machine to learn and predict the unknown sample class label. In this paper, a novel feature extraction mechanism is proposed and named as Counting Label Occurrence Matrix (CLOM). CLOM is based on the counting label of the gray level intensity values of an image and used to extract the textural features of an image for image classification. Four different orientations are used in CLOM for extracting features based on anticipated algorithm. Proposed feature extraction mechanism is dynamic in nature and is used in any domain of image processing and machine learning. CLOM algorithm is compared with some feature extraction algorithms like GLCM (gray level co-occurrence matrix) and Run length Matrix when CLOM is tested in medical domain for classification of brain tumor images. The experimental result shows that the proposed algorithm gives better accuracy than past algorithms when tested with classifiers like KNN and BPNN.

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Section: Related Workmentioning

confidence: 99%

Section: Classificationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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CLOM: Counting label occurrence matrix for feature extraction in MR images

Nagpal

Vidyarthi

Mittal

2015

2015 International Conference on Signal Processing and Communication (ICSC)

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“…In [9], author describes the comparative study for the classification of brain tumors which was done in past for the duration of 2009 to 2013. Comparative study gives the detail of the work done in past for tumor classification using different feature extraction and selection techniques with accuracy.…”

Section: Related Workmentioning

confidence: 99%

Performance analysis of Gabor-Wavelet based features in classification of high grade malignant brain tumors

Vidyarthi

Mittal

2015

2015 39th National Systems Conference (NSC)

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Incorporating machine learning based concepts and knowledge in medical science domain elaborates the interdisciplinary research areas. Using such the identification of the disease is accomplished at initial state of its generation with better accuracy as compared to naked eye. In past, lot of feature extraction approaches were proposed to extract the features in spectral domain. Among all approaches, two major approaches was Gabor filter based feature extraction and Wavelet based feature extraction mechanism. But it leads to the kiosk that which feature extraction mechanism is better for classification perspective. This paper focuses on machine learning based approach for performance analysis for the grade of malignant tumor types using a diverse feature set based on Gabor and Wavelet transformation. Further, various feature selection (FS) algorithms are taken into consideration for selection of the best feature set among the feature vector. For experimental purpose, several state-of-art classifiers are used for analysis of the performance for the classification of malignant brain tumors in magnetic resonance (MR) images. In experimentation several high grade malignant brain tumors like Central Neuro Cytoma (CNC), Glioblastoma Multiforme (GBM), Gliomas, Intra Ventricular Malignant Mass, and Metastasis are taken into consideration having 30 images of each tumor type. The classification accuracies achieved using various combinations of FS-Classifier's are presented in detail.

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“…EL-Sayed developed a hybrid methodology for classification of brain MR images [4]. In order to analyze medical images, especially brain images, and classify the type of tumor, the determination of tissue type i.e., abnormal or normal and tissue pathology is essential [5]. The technique consists of three main steps: feature extraction using DWT, dimensionality reduction using PCA and classification using ANN and k-NN.…”

Section: Introductionmentioning

confidence: 99%

5 Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images

Farhi¹,

Zia²,

Ali³

2018

SSURJ

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Brain cancer has remained one of the key causes ofdeaths in people of all ages. One way to survival amongst patientsis to correctly diagnose cancer in its early stages. Recentlymachine learning has become a very important tool in medicalimage classification. Our approach is to examine and comparevarious machine learning classification algorithms that help inbrain tumor classification of Magnetic Resonance (MR) images.We have compared Artificial Neural Network (ANN), K-nearestNeighbor (KNN), Decision Tree (DT), Support Vector Machine(SVM) and Naïve Bayes (NB) classifiers to determine theaccuracy of each classifier and find the best amongst them forclassification of cancerous and noncancerous brain MR images.We have used 86 MR images and extracted a large number offeatures for each image. Since the equal number of images, havebeen used thus there is no suspicion of results being biased. Forour data set the most accurate results were provided by ANN. Itwas found that ANN provides better results for medium to largedatabase of Brain MR Images.

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Comparative Study for Brain Tumor Classification on MR/CT Images

Cited by 15 publications

References 16 publications

CLOM: Counting label occurrence matrix for feature extraction in MR images

CLOM: Counting label occurrence matrix for feature extraction in MR images

Performance analysis of Gabor-Wavelet based features in classification of high grade malignant brain tumors

5 Performance Analysis of Machine Learning Classifiers for Brain Tumor MR Images

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