Page 12 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

About the book

Deep learning (DL) is a method of machine learning, as
running over artificial neural networks, which has a structure
above the standards to deal with large amounts of data. This is
generally because of the increasing amount of data, input data
sizes, and, of course, greater complexity of objective real-world
problems. Research studies performed in the associated litera-
ture show that the DL currently has a good performance among
considered problems and seems to be a strong solution for more
advanced problems of the future. In this context, this book aims to
provide some essential information about DL and its applications
within the field of biomedical engineering. Due to numerous
biomedical information sensing devices, such as computed
tomography, magnetic resonance imaging, ultrasound, single
photon emission computed tomography, positron emission
tomography, magnetic particle imaging, EE/MEG, optical mi-
croscopy and tomography, photoacoustic tomography, electron
tomography, and atomic force microscopy, large amount of
biomedical information was gathered these years. This poses a
great challenge on how to develop new advanced imaging
methods and computational models for efficient data process-
ing, analysis, and modeling in clinical applications and in
understanding the underlying biological process.