FDN-ADNet: Fuzzy LS-TWSVM based deep learning network for prognosis of the Alzheimer's disease using the sagittal plane of MRI scans

Abstract

Alzheimer's disease (AD) is the most pervasive form of dementia, resulting in severe psychosocial effects such as affecting personality, reasoning, emotions, and memory. Several neuroimaging techniques are available to correctly identify the structural changes in the brain, out of which the most popular is structural T-1 weighted Magnetic Resonance Imaging (MRI). From 3D MRI, sagittal plane slices provide more clear information related to the hippocampus, amygdala, corpus callosum, and several vital regions of the brain, which defines the extent of degeneration of the AD. Although diverse analysis of machine learning (ML) and deep learning (DL) based algorithm is already proposed for diagnosis of AD, still there is scope of research for early prediction so that treatment can be started either by medication or by improving the lifestyle. This paper proposed a DL model for all level feature extraction and fuzzy hyperplane based least square twin support vector machine (FLS-TWSVM) for the classification of the extracted features for early diagnosis of AD (FDN-ADNet) using extracted sagittal plane slices from 3D MRI images. Model is trained over the online available ADNI dataset and triangular fuzzy function is applied for the construction of hyperplane for classification. The proposed model attains the highest accuracy of 97.15%, 97.29% and 95% for CN vs AD, CN vs MCI and AD vs MCI classification, respectively when compared with the several state of the art networks. © 2021 Elsevier B.V.