A systematic review of vision transformers and convolutional neural networks for Alzheimer’s disease classification using 3D MRI images

Abstract

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that mainly affects memory and other cognitive functions, such as thinking, reasoning, and the ability to carry out daily activities. It is considered the most common form of dementia in older adults, but it can appear as early as the age of 25. Although the disease has no cure, treatment can be more effective if diagnosed early. In diagnosing AD, changes in the brain’s morphology are identified macroscopically, which is why deep learning models, such as convolutional neural networks (CNN) or vision transformers (ViT), excel in this task. We followed the Systematic Literature Review process, applying stages of the review protocol from it, which aims to detect the need for a review. Then, search equations were formulated and executed in several literature databases. Relevant publications were scanned and used to extract evidence to answer research questions. Several CNN and ViT approaches have already been tested on problems related to brain image analysis for disease detection. A total of 722 articles were found in the selected databases. Still, a series of filters were performed to decrease the number to 44 articles, focusing specifically on brain image analysis with CNN and ViT methods. Deep learning methods are effective for disease diagnosis, and the surge in research activity underscores its importance. However, the lack of access to repositories may introduce bias into the information. Full access demonstrates transparency and facilitates collaborative work in research.