Deep learning methods to detect Alzheimer's disease from MRI: A systematic review

• Tipo: Outra Publicação em Revista Científica Internacional
• Data: 2023-01

Autores

Mariana Coelho (Autor) Outra; Martin Cerny (Autor) Outra; João Manuel R. S. Tavares (Autor) FEUP

Revista

Título: Expert Systems

Vol. 42 Nº 1

Páginas: e13463-1-23

ISSN: 0266-4720

Editora: Wiley-Blackwell

Indexação

ISI Web of Knowledge - 0 Citações

ISI Web of Science

Scopus - 0 Citações

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Classificação Científica

CORDIS: Ciências Tecnológicas

FOS: Ciências médicas e da saúde

Outras Informações

ID Authenticus: P-00Z-5XH

DOI: 10.1111/exsy.13463

Abstract (EN): Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative condition in the brain that affects memory, thinking, and behaviour. To overcome this problem, which according to the World Health Organization, is on the rise, creating strategies is essential to identify and predict the disease in its early stages before clinical manifestation. In addition to cognitive and mental tests, neuroimaging is promising in this field, especially in assessing brain matter loss. Therefore, computer-aided diagnosis systems have been imposed as fundamental tools to help imaging technicians as the diagnosis becomes less subjective and time-consuming. Thus, machine learning and deep learning (DL) techniques have come into play. In recent years, articles addressing the topic of Alzheimer's diagnosis through DL models are increasingly popular, with an exponential increase from year to year with increasingly higher accuracy values. However, the disease classification remains a challenging and progressing issue, not only in distinguishing between healthy controls and AD patients but mainly in differentiating intermediate stages such as mild cognitive impairment. Therefore, there is a need to develop more valuable and innovative techniques. This article presents an up-to-date systematic review of deep models to detect AD and its intermediate phase by evaluating magnetic resonance images. The DL models chosen by different authors are analysed, as well as their approaches regarding the used dataset and the data pre-processing and analysis techniques.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 23