An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry
CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE, vol.19, no.2, pp.206-219, 2021 (SCI-Expanded, Scopus)
- Publication Type: Article / Review
- Volume: 19 Issue: 2
- Publication Date: 2021
- Doi Number: 10.9758/cpn.2021.19.2.206
- Journal Name: CLINICAL PSYCHOPHARMACOLOGY AND NEUROSCIENCE
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, EMBASE, Psycinfo
- Page Numbers: pp.206-219
- Keywords: Deep learning, Neuropsychiatry, Artificial neural networks, Convolutional neural networks, Recurrent neural networks, Generative adversarial networks, ALZHEIMERS-DISEASE, BACKPROPAGATION, CLASSIFICATION, DIAGNOSIS
- Open Archive Collection: AVESIS Open Access Collection
- Ankara University Affiliated: Yes
Abstract
Deep learning (DL) algorithms have achieved important successes in data analysis tasks, thanks to their capability of revealing complex patterns in data. With the advance of new sensors, data storage, and processing hardware, DL algorithms start dominating various fields including neuropsychiatry. There are many types of DL algorithms for different data types from survey data to functional magnetic resonance imaging scans. Because of limitations in diagnosing, estimating prognosis and treatment response of neuropsychiatric disorders; DL algorithms are becoming promising approaches. In this review, we aim to summarize the most common DL algorithms and their applications in neuropsychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.