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An approach for brain tumour detection based on dual-tree complex Gabor wavelet transform and neural network using Hadoop big data analysis

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Abstract

Segmentation and classification of the abnormalities on the brain are necessary to save one’s life; hence the data acquired by magnetic resonance imaging (MR imaging) scan have to be processed. Handling massive MR imaging data for high accuracy and precision is a major concern for any framework. Big data and image processing are integrated for brain tumor classification and segmentation in this work. The Hadoop system on MATLAB performs the big data analysis of the brain tumor image. The BraTS dataset is provided to the Hadoop and Matlab distributed computing server (MDCS) system for processing, processed by the single master node and four slave nodes (multimode) on the MDCS configuration. The data from this analysis is decomposed by the novel dual-tree complex Gabor wavelet transform (DTCGWT). The resulting feature vectors are classified as malignant and benign brain tumors based on the deep convolutional neural network (DCNN). If a malignant brain tumor is classified, then the fuzzy level set method based on the manta ray foraging algorithm (FLSM-MRF) will segment the portions of the brain tumor. The model is implemented in the MATLAB platform and has yield minimum of 56.8 min for processing ~30GB of data, while on image processing, 99.1234% and 99.15% accurate result for classification and segmentation respectively is obtained. The parameters like accuracy, sensitivity, specificity, dice, and Jaccard similarity indexes are compared with the existing methods.

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Authors and Affiliations

  1. NTT DATA Canada, Halifax, Canada

    Santhosh Kumar H S

  2. Graphic Era Deemed to be University, Dehradun, Uttarakhand, 248002, India

    K. Karibasappa

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  1. Santhosh Kumar H S
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  2. K. Karibasappa
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Correspondence to Santhosh Kumar H S.

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Santhosh Kumar H S, Karibasappa, K. An approach for brain tumour detection based on dual-tree complex Gabor wavelet transform and neural network using Hadoop big data analysis. Multimed Tools Appl 81, 39251–39274 (2022). https://doi.org/10.1007/s11042-022-13016-6

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