LAUSR

Early diagnosis is critical for the development and success of interventions, and neuroimaging is one of the most promising areas for early detection of Alzheimer’s disease (AD). This study is aimed to develop a deep learning method to extract useful AD biomarkers from structural magnetic resonance imaging (sMRI) and classify brain images into AD, mild cognitive impairment (MCI) and cognitively normal (CN) groups. In this work, we adapted and trained convolutional neural networks (CNNs) on sMRI images of the brain from ADNI datasets available in online databases. Our proposed mechanism was used to combine features from different layers to hierarchically transform the images from magnetic resonance imaging into more compact high-level features. The proposed method has reduced number of parameters which reduces the computation complexity. The method is compared with the existing state-of-the-art works for AD classification, which show superior results for the widely used evaluation metrics including accuracy, area under the ROC curve etc., suggesting that our proposed convolution operation is suitable for the AD diagnosis.