An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry
| Autor: | Busra Ozgode Yigin, Burçin Çolak, Gorkem Saygili, Gamze Erzin, Yasemin Hosgoren Alici, Necdet Guven, Gokhan Guney |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Treatment response
Review Neuropsychiatry Convolutional neural network Data type 03 medical and health sciences Behavioral Neuroscience 0302 clinical medicine Medicine Pharmacology (medical) 030304 developmental biology 0303 health sciences Artificial neural networks Artificial neural network business.industry Deep learning Generative adversarial networks Psychiatry and Mental health Recurrent neural network Recurrent neural networks Computer data storage Convolutional neural networks Artificial intelligence business Algorithm 030217 neurology & neurosurgery |
| Zdroj: | Clinical Psychopharmacology and Neuroscience |
| ISSN: | 2093-4327 1738-1088 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|