A Deep Convolutional Neural Networks Based Approach for Alzheimer’s Disease and Mild Cognitive Impairment Classification Using Brain Images

Abstract

Alzheimer’s disease (AD) is a hazardous neurological disorder of people aged in the early 60s. The main symptoms of AD is significant memory loss. Mild Cognitive Impairment (MCI) is a state of dementia in which a patient exhibits the early symptoms of AD. Since brain is the most impacted region, the disorders can be classified by analyzing factors from brain tissues in different subjects. Machine Learning (ML) is a widely utilised concept that aids in the decision-making process. Deep Convolutional Neural Network (DNN) is a type of ML techniques that uses artificially connected neurons to mimic the human brain. In this work, we have proposed a novel DNN-based model for distinguishing AD and MCI patients from Cognitively Normal individuals. Inspired by the original VGG-19, we have created 19 deep layers in the network. In Back Propagation, deeper models suffer from the problem of vanishing gradient and information loss. As a solution, we borrowed the Dense-Block notion from the original DenseNet architecture, which provides a path of information exchange amongst all the layers. Furthermore, we have implemented depth-wise convolutional procedures to make the model computationally faster. Outcome of the proposed model is compared with some prominent DNN models and observed that, the proposed approach performs most convincingly with an average performance rate of 95.39%.

Keywords

• Alzheimer’s disease (AD)
• mild cognitive impairment (MCI)
• deep convolutional neural network (DNN)
• cognitively normal (CN)
• machine learning (ML)
• DenseNet