A computer‐aided approach for meningioma brain tumor detection using C<scp>ANFIS</scp> classifier

Kathirvel, R.; Batri, K.

doi:10.1002/ima.22223

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…The proposed meningioma brain tumor detection methodology stated in this article achieves 93.46% of sensitivity, 96.54% of specificity, and 97.75% of accuracy as depicted in Table . Table shows the performance analysis of proposed brain tumor segmentation methodology on BrainWeb data set in terms of sensitivity, specificity, accuracy, precision, recall, and F ‐measure with different conventional methodologies…”

Section: Resultsmentioning

confidence: 99%

“…The segmentation gap between radiologist segmented tumor region and automatically segmented tumor region was low which was measured by 0.82 dice similarity index coefficient. Kathirvel and Batri proposed an efficient methodology for the detection of Meningioma brain tumors. The authors extracted texture features from the source brain MRI image and then these extracted features were classified using co‐active adaptive neuro fuzzy inference system (CANFIS) classification approach with respect to trained patterns.…”

Section: Literature Surveymentioning

confidence: 99%

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Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier

Selvapandian

Karunakaran

2018

Int J Imaging Syst Tech

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Detection of abnormal regions in brain image is complex process due to its similarity between normal and abnormal regions. This article proposes an automated technique for the detection of meningioma tumor using Gradient Boosting Machine Learning (GBML) classification method. This proposed system consists of preprocessing, feature extraction and classification stages. In this article, Grey Level Co occurrence Matrix (GLCM) features, intensity features, and Gray Level Run Length Matrix features are derived from the test brain MRI image. These derived feature set are classified using GBML classification approach. Morphological functions are used to segment the tumor region in classified abnormal brain image. The performance of the proposed system is evaluated on brain MRI images which are obtained from open access data set. The proposed methodology stated in this article achieves 93.46% of sensitivity, 96.54% of specificity, and 97.75% of accuracy with respect to ground truth images. K E Y W O R D S brain image,

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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier

Selvapandian

Karunakaran

2018

Int J Imaging Syst Tech

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“…For Epilepsy, Jeong et al [60] aimed at devising a novel clustering approach for MEG interictal spike sources and identifying its potential value in adult epilepsy patients with cortical dysplasia. For Computer-Aided Diagnosis, Kathirvel and Batri [61] proposed an innovative fully-automated computer-assisted approach to detect brain tumor with the use of co-active adaptive neuro-fuzzy inference system classifier. For EEG Signals Analysis, Li et al [62] proposed an innovative hybrid automated sleep stage scoring method called HyCLASSS with the basis of single channel EEG.…”

Section: Plos Onementioning

confidence: 99%

Topics and trends in artificial intelligence assisted human brain research

Chen

Cheng

et al. 2020

PLoS ONE

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Artificial intelligence (AI) assisted human brain research is a dynamic interdisciplinary field with great interest, rich literature, and huge diversity. The diversity in research topics and technologies keeps increasing along with the tremendous growth in application scope of AIassisted human brain research. A comprehensive understanding of this field is necessary to assess research efficacy, (re)allocate research resources, and conduct collaborations. This paper combines the structural topic modeling (STM) with the bibliometric analysis to automatically identify prominent research topics from the large-scale, unstructured text of AIassisted human brain research publications in the past decade. Analyses on topical trends, correlations, and clusters reveal distinct developmental trends of these topics, promising research orientations, and diverse topical distributions in influential countries/regions and research institutes. These findings help better understand scientific and technological AIassisted human brain research, provide insightful guidance for resource (re)allocation, and promote effective international collaborations.

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“…Kathirvel & Batri 14 proposed a CAD method formed on contourlet transform as well as a coactive adaptive neuro fuzzy inference system (CANFIS). The contourlet transform was used to decompose the images from the dataset at different scales to extract the gray level as well as heuristic features.…”

Section: Literature Surveymentioning

confidence: 99%

Fractional wavelet transform based diagnostic system for brain tumor detection in MR imaging

Kaushal¹,

Patil²,

Birajdar

2020

Int J Imaging Syst Tech

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The brain tumor detection is a highly complicated but significant task. The early detection of a brain tumor can increase the survival rate of an individual by providing proper treatment. This work proposes a computer‐aided diagnostic method for brain tumor detection using fractional wavelet transform (FrDWT) with different values of alpha (α) ranging from (0.1‐1), histogram‐based various local feature descriptors, feature selectors, and two classification methods, that is, support vector machine (SVM) as well as artificial neural network (ANN). The brain MR images dataset is taken from BraTS 2015, e‐health laboratory, and Harvard Medical School. The FrDWT and the local feature descriptors used are combined to extract features. Some of the features are selected using Eigenvector centrality and Laplacian Score techniques. The selected features are trained and classified by the two classifiers, SVM and ANN. It is a type of binary classification, so the labels provided to the classifier are named “normal” and “abnormal.” The performance is estimated using parameters like accuracy, sensitivity, precision, specificity, and F1 score. The results of FrDWT are compared with conventional discrete wavelet transform (DWT), and it is observed that FrDWT outperforms DWT at alpha (α) values lower than 0.5.

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A computer‐aided approach for meningioma brain tumor detection using CANFIS classifier

Cited by 7 publications

References 10 publications

Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier

Performance analysis of meningioma brain tumor classifications based on gradient boosting classifier

Topics and trends in artificial intelligence assisted human brain research

Fractional wavelet transform based diagnostic system for brain tumor detection in MR imaging

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