Bayesian HCS-based multi-SVNN: A classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering

• Autorzy: Raju A. R. , Suresh P. , Rao R. R.

Identyfikatory

DOI

10.1016/j.bbe.2018.05.001

• Języki publikacji: EN

Abstrakty

EN

Brain tumor segmentation and classification is the interesting area for differentiating the tumerous and the non-tumerous cells in the brain and to classify the tumerous cells for identifying its level. The conventional methods lack the automatic classification and they consumed huge time and are ineffective in decision-making. To overcome the challenges faced by the conventional methods, this paper proposes the automatic method of classification using the Harmony-Crow Search (HCS) Optimization algorithm to train the multi-SVNN classifier. The brain tumor segmentation is performed using the Bayesian fuzzy clustering approach, whereas the tumor classification is done using the proposed HCS Optimization algorithm-based multi-SVNN classifier. The proposed method of classification determines the level of the brain tumor using the features of the segments generated based on Bayesian fuzzy clustering. The robust features are obtained using the information theoretic measures, scattering transform, and wavelet transform. The experimentation performed using the BRATS database conveys proves the effectiveness of the proposed method and the proposed HCS-based tumor segmentation and classification achieves the classification accuracy of 0.93 and outperforms the existing segmentation methods.

Słowa kluczowe

EN

MRI image; brain tumor classification; brain tumor segmentation; Bayesian fuzzy clustering; support vector neural network

PL

obraz MRI; guz mózgu; klasteryzacja rozmyta; sieć neuronowa

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2018
• Tom: Vol. 38, no. 3
• Strony: 646--660
• Opis fizyczny: Bibliogr. 41 poz., rys., tab., wykr.

Twórcy

autor

Raju A. R.

• Mahatma Gandhi Institute of Technology, Hyderabad 500075, India

autor

Suresh P.

• Chaitanya Bharathi Institute of Technology, Hyderabad, Telangana, India

autor

Rao R. R.

• JNTUK-UCEV, Andhra Pradesh, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).