Deep Learning-Based Ensembling Technique to Classify Alzheimer's Disease Stages Using Functional MRI

Abstract

The major issue faced by elderly people in society is the loss of memory, difficulty learning new things, and poor judgment. This is due to damage to brain tissues, which may lead to cognitive impairment and eventually Alzheimer's. Therefore, the detection of such mild cognitive impairment (MCI) becomes important. Usually, this is detected when it is converted into Alzheimer's disease (AD). AD is irreversible and cannot be cured whereas mild cognitive impairment (MCI) can be cured. The goal of this research is to diagnose Alzheimer's patients for timely treatment. For this purpose, functional MRI images from the publicly available dataset are used. Various deep-learning models have been used by the scientific community for the automatic detection of Alzheimer's subjects. These include the binary classification of scans of patients into MCI and AD stages, and limited work is carried out for multiclass classification of Alzheimer's disease up to six different stages. This study is divided into two steps. In the first step, a binary classification of the subject's scan is performed using Custom CNN. The second step involves the use of different deep learning models along with Custom CNN for multiclass classification of a subject's scan into one of the six stages of Alzheimer's disease. The models are evaluated based on different evaluation metrics, and the overall result of the models is improved using the max-voting ensembling technique. The experimental results show that an overall average accuracy of 98.8% is achieved for Alzheimer's stages classification.