A CAD system design for Alzheimer's disease diagnosis using temporally consistent clustering and hybrid deep learning models

Abstract

Alzheimer Disease (AD) is a permanent brain syndrome that is triggered due to the dynamic deterioration of memory and cognitive functions. This paper presents a new computer-aided diagnosis (CAD) system to diagnose AD using Magnetic resonance imaging (MRI) data. Although the available AD diagnosis systems show good results, they did not capture the subtle changes of the disease due to inconsistencies in segmentation, feature extraction and classification processes. In the proposed CAD system, the tissues are initially segmented by proposing a new Temporally Consistent Black widow optimization (BWO) combined Fuzzy C-Means Clustering (FCM) clustering (TC-BW-FCM) segmentation method. It introduces temporal consistency constraints to address the temporal changes in intensity homogeneities by considering each tissue's bias field and intensity means. Also, a hybrid Texture, Edge, Color and density (TECD) feature extraction approach is combined with clinical data to give data about the emotional stage of the patient. A hybrid Rotation Forest Deep Neural Network (HRF-DNN) is proposed to improve the classification accuracy and used rotation forest to generate training feature subset for the Deep enhanced stacked auto encoder (DESAE) classifier. The simulation results show that the proposed CAD system outperforms the existing systems by increasing the accuracy, sensitivity, and specificity to 98.68%, 97.72% and 97.19%, respectively, for multi-class problems.