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An effective hybrid deep learning with adaptive search and rescue for brain tumor detection

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Abstract

Medical image processing is a challenging and complex field. Moreover, the brain tumor is one of the significant factors for death in human beings. Therefore, detection of brain tumor at the initial stage is very essential. In this paper, a hybrid deep belief neural network with adaptive search and rescue algorithm (DBN-ASAR) is proposed to classify the tumor images and the normal images from the MRI brain images. Pre-processing is the initial process, in which the Fuzzy based Fast averaging peer group (FFAPG) filtering approach is proposed to eliminate the impulsive noises from the MRI brain images. In features extraction, discrete wavelet transform with Gabor filter (DWT-GF) is used to extract the texture and shape features from the images effectively. From these extracted features, only the optimal features are selected to decrease the dimensionality. Hybrid feature selection approach is proposed to obtain the optimal features and dimensionality reduction. Next phase is the classification, in which the selected features are trained using deep belief network (DBN). Adaptive search and rescue (ASAR) algorithm is combined with DBN model to obtain the optimal classification solution; thus the normal and tumor images are classified. From the classified images, only the tumor images are fed as input for the segmentation process. Using threshold with tree growth algorithm (TTGA), the tumor region is detected effectively from the tumor images. MATLAB tool is used for the experimentation of proposed model. Figshare, BRATS 2013, 2015 and 2018 datasets are utilized for the evaluation of proposed work. Accuracy, precision, sensitivity, F-measure, specificity, DSC, NPV and FPR are the performance metrics considered to evaluate the efficiency of the proposed approach. The simulation results proved that the performance of the proposed method is better than the state-of-the-art 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, 248002, Uttarakhand, 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 effective hybrid deep learning with adaptive search and rescue for brain tumor detection. Multimed Tools Appl 81, 17669–17701 (2022). https://doi.org/10.1007/s11042-022-12474-2

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  • DOI: https://doi.org/10.1007/s11042-022-12474-2

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