Brain tumor classification using a hybrid deep autoencoder with Bayesian fuzzy clustering-based segmentation approach

• Autorzy: Siva Raja P. M. , Rani Antony Viswasa

Identyfikatory

DOI

10.1016/j.bbe.2020.01.006

• Języki publikacji: EN

Abstrakty

EN

In medical image processing, brain tumor detection and segmentation is a challenging and time-consuming task. Magnetic Resonance Image (MRI) scan analysis is a powerful tool in the recent technology that makes effective detection of the abnormal tissues from the brain. In the brain image, the size of a tumor can be varied for different patients along with the minute details of the tumor. It is a difficult task to diagnose and classify the tumor from numerous images for the radiologists. This paper developed a brain tumor classification using a hybrid deep autoencoder with a Bayesian fuzzy clustering-based segmentation approach. Initially, the pre-processing stage is performed using the non-local mean filter for denoising purposes. Then the BFC (Bayesian fuzzy clustering) approach is utilized for the segmentation of brain tumors. After segmentation, robust features such as, information-theoretic measures, scattering transform (ST) and wavelet packet Tsallis entropy (WPTE) methods are used for the feature extraction process. Finally, a hybrid scheme of the DAE (deep autoencoder) based JOA (Jaya optimization algorithm) with a softmax regression technique is utilized to classify the tumor part for the brain tumor classification process. The proposed scheme is implemented in a MATLAB environment. The simulation results are conducted by the BRATS 2015 database which proved that the proposed approach obtained the high classification accuracy (98.5 %) when compared to other state-of-art methods.

Słowa kluczowe

EN

brain tumor segmentation; brain tumor classification; deep autoencoder; Bayesian fuzzy clustering; magnetic resonance image

PL

segmentacja guza mózgu; klasyfikacja guza mózgu; autoenkoder; rezonans magnetyczny

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2020
• Tom: Vol. 40, no. 1
• Strony: 440--453
• Opis fizyczny: Bibliogr. 51 poz., rys., tab., wykr.

Twórcy

autor

Siva Raja P. M.

• Computer Science and Engineering, Amrita College of Engineering and Technology, Amritagiri, Erachakulam, Tamil Nadu, India

autor

Rani Antony Viswasa

• Computer Science and Engineering, Amrita College of Engineering and Technology, Amritagiri, Erachakulam, Tamil Nadu, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).