Deep feature extraction method based on ensemble of convolutional auto encoders: Application to Alzheimer’s disease diagnosis

Abstract

Alzheimer’s disease is one of the famous causes of death among elderly. Diagnosis of this disease in the early stage is so difficult by conventional methods. Machine learning methods are one of the best choice for improving the accuracy and performance of diagnosis procedure. The heterogeneous dimensions and structure among the data of this disease have complicated the diagnosis process. Therefore proper features are needed to solve this complexity. In this research, proposed method is introduced in two main steps. In the first step, ensemble of pre-trained auto encoder based feature extraction modules are used to generate image feature from 3D input image and in the second step convolutional neural network is used to diagnosis Alzheimer’s disease. Three different classification cases, namely; Alzheimer’s Disease (AD) versus Normal Condition (NC), AD versus Mild Cognitive Impairment (MCI) and MCI versus NC are studied. Obtained results show that accuracy rate for AD/NC, AD/MCI and MCI/NC are 95%, 90% and 92.5%, respectively. Also, for all cases sensitivity and specially sensitivity rates for proposed method confirm that it could be reliable for diagnosis AD in early stage and has less error to detect normal condition.