Classification of Alzheimer’s Disease Using Stacking-Based Ensemble and Transfer Learning

Abstract

Dementia is collection of traits that are linked with a reduced memory power, thinking abilities or other cognition-related skills. Dementia exists in many different forms and can be caused by many different conditions. However, the most common cause of dementia is Alzheimer’s. Alzheimer’s can be described as one specific disease (Beer et al in The Merck manual of diagnosis and therapy. Merck Research Laboratories, 1999 [1]). In recent years, many deep learning approaches to classify brain images with respect to the Alzheimer’s disease are being proposed, and a tremendous amount of research is being done in this area. However, these approaches are still not being used actively in the medical field due to apprehensions about their accuracy and due to a general lack of appropriate medical data. This paper aims at introducing an approach to classify the Alzheimer’s MRI image data into four different stages. The approach produces efficient and accurate results and is designed to encourage the implementation of deep learning in day-to-day medicine without the need for much human involvement. In the proposed method, we use transfer learning to employ three pre-trained deep learning models to perform the task of classification and combine them through a stacked ensemble, which can then be used for the purpose of predictions. To carry out this approach, the Alzheimer’s data set (four classes of images) from Kaggle, containing MRI brain images, has been used, and the proposed methodology has produced an accuracy of 97.8%. The results have been visualized and presented in this paper.