
An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry
- 대한정신약물학회
- Clinical Psychopharmacology and Neuroscience
- Vol.19 No.2
- : SCOPUS, SCIE, KCI등재
- 2021.05
- 206 - 219 (14 pages)
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 algo-rithms 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, esti-mating 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 neuro-psychiatry and also provide an overview to guide the researchers in choosing the proper DL architecture for their research.
INTRODUCTION
DEEP LEARNING CONCEPTS AND ARCHITECTURES
APPLICATION OF DEEP LEARNING ALGORITHMS TO NEUROPSYCHIATRIC DISORDERS
DISCUSSION
FUTURE ASPECTS
CONCLUSION
REFERENCES