An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry
10.9758/cpn.2021.19.2.206
- Author:
Gokhan GUNEY
1
;
Busra Ozgode YIGIN
;
Necdet GUVEN
;
Yasemin Hosgoren ALICI
;
Burcin COLAK
;
Gamze ERZIN
;
Gorkem SAYGILI
Author Information
1. Department of Biomedical Engineering, Ankara University, Ankara, Turkey
- Publication Type:Review
- From:Clinical Psychopharmacology and Neuroscience
2021;19(2):206-219
- CountryRepublic of Korea
- Language:English
-
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.
