Brain Tumor Identification by Hybrid CNN-SWT Model

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dc.contributor.authorAbd El Kader, Isselmou
dc.contributor.authorXu, Guizhi
dc.contributor.authorZhang, Shuai
dc.contributor.authorSaminu, Sani
dc.contributor.authorJavaid, Imran
dc.contributor.authorAhmad, Isah Salim
dc.contributor.authorKamhi, Souha
dc.date.accessioned2023-08-28T09:55:28Z
dc.date.available2023-08-28T09:55:28Z
dc.date.issued2022-05-24
dc.description.abstractObjective: Detecting brain tumor using the segmentationtechnique is a big challenge for researchers and takes a long time inmedical image processing. Magnetic resonance image analysis techniquesfacilitate the accurate detection of tissues and abnormal tumors in thebrain. The size of a brain tumor can vary with the individual and thespecifics of the tumor. Radiologists face great difficulty in diagnosing andclassifying brain tumors. Method: This paper proposed a hybrid model-based convolutional neuralnetwork with a stationary wavelet trans-form named “CNN-SWT” tosegment brain tumors using MR brain big data. We utilized 7 layers forclassification in the proposed model that include 3 convolutional and 3ReLU. Firstly, the input MR image is divided into multiple patches, and thenthe central pixel value of each patch is provided to the CNN-SWT. Secondly,the pre-processing stage is per-formed using the mean filter to remove thenoise. Then the convolution neural network-layer approach is utilized tosegment brain tumors. After segmentation, robust feature extraction suchas information-extraction methods is used for the feature extractionprocess. Finally, a CNN-based hybrid scheme based on the stationarywavelet transform technique is used to detect tumors using MR brainimages. Materials: These experiments were obtained using 11500 MR brain imagesdata from the hospital national of oncology. Results: It was proved that the proposed hybrid achieved a highclassification accuracy of (98.7 %) as compared with existing methods. Conclusion: The advantage of the hybrid novelty of the model and theability to detect the tumor area achieved excellent overall performanceusing different values.en_US
dc.identifier.urihttps://uilspace.unilorin.edu.ng/handle/20.500.12484/11694
dc.language.isoenen_US
dc.publisherBentham Science Publishers Ltden_US
dc.subjectBrain detection , classification , MR Images , convolution neuralnetwork , stationary wavelet transform , tumoren_US
dc.titleBrain Tumor Identification by Hybrid CNN-SWT Modelen_US
dc.typeArticleen_US

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