An Overview of Deep Learning Algorithms and Their Applications in Neuropsychiatry
Gokhan Guney , Busra Ozgode Yigin , Necdet Guven , Yasemin Hosgoren Alici , Burcin Colak , Gamze Erzin , Gorkem Saygili *
1Ankara University, Department of Biomedical Engineering, Ankara, 2Baskent University, Psychiatry Department, Ankara, 3Ankara University, Psychiatry Department, Ankara , 4Ankara Diskapi Yildirim Beyazit Training and Research Hospital, Psychiatry Department, Ankara
Received: June 28, 2020; Revised: August 31, 2020; Accepted: September 5, 2020; Published online: September 5, 2020.
© The Korean College of Neuropsychopharmacology. All rights reserved.

Abstract
Deep learning 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, deep learning algorithms start dominating various fields including neuropsychiatry. There are many types of deep learning algorithms for different data types from survey data to fMRI scans. Because of limitations in diagnosing, estimating prognosis, and treatment response of neuropsychiatric disorders; deep learning algorithms are becoming promising approaches. In this review, we aim to summarize the most common deep learning algorithms and their applications in neuropsychiatry and also provide an overview to guide the researchers in choosing the proper deep learning architecture for their research.
Keywords: deep learning, deep learning, neuropsychiatry, neuropsychiatry, artificial neural networks, artificial neural networks, convolutional neural networks, convolutional neural networks, recurrent neural networks, recurrent neural networks, generative advesari


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