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Detection of Alzheimer's disease using grey wolf optimization based clustering algorithm and deep neural network from magnetic resonance images

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DISTRIBUTED AND PARALLEL DATABASES 2022年第4期40卷 627-655页

作者： Suresha, Halebeedu Subbaraya Parthasarathy, Srirangapatna Sampathkumaran Univ Mysore PET Res Ctr Dept ECE Mysore Karnataka India PES Coll Engn Dept ECE Mandya India

The automated magnetic resonance imaging (MRI) processing techniques are gaining more importance in Alzheimer disease (AD) recognition, because it effectively diagnosis the pathology of the brain. Currently, computer aided diagnosis based on image analysis is an emerging tool to support AD diagnosis. In this research study, a new system is developed for enhancing the performance of AD recognition. Initially, the brain images were acquired from three online datasets and one real-time dataset such as AD Neuroimaging Initiative (ADNI), Minimal Interval Resonance Imaging in AD (MIRIAD), and Open Access Series of Imaging Studies (OASIS) and National Institute of Mental Health and Neuro Sciences (NIMHANS). Then, adaptive histogram equalization (AHE) and grey wolf optimization based clustering algorithm (GWOCA) were applied for denoising and segmenting the brain tissues;grey matter (GM), cerebro-spinal fluid (CSF), and white matter (WM) from the acquired images. After segmentation, the feature extraction was performed by utilizing dual tree complex wavelet transform (DTCWT), local ternary pattern (LTP) and Tamura features to extract the feature vectors from the segmented brain tissues. Then, ReliefF methodology was used to select the active features from the extracted feature vectors. Finally, the selected active feature values were classified into three classes [AD, normal and mild cognitive impairment (MCI)] utilizing deep neural network (DNN) classifier. From the simulation result, it is clear that the proposed framework achieved good performance in disease classification and almost showed 2.2-6% enhancement in accuracy of all four datasets.

关键词： Alzheimer disease recognition and classification Deep neural network grey wolf optimization based clustering algorithm Histogram equalization ReliefF algorithm

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