Page 254 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

P. 254

Applications of deep learning in

biomedical engineering

S. Shajun Nisha*, M. Nagoor Meeral
PG & Research Department of Computer Science, Sadakathullah Appa
College, Tirunelveli, Tamil Nadu, India
*Corresponding author:Shajun Nisha[shajunnisha78@gmail.com].

1. Introduction
Biomedical engineering can also be called as bioengineering
or BME. It is a multidisciplinary activity applying engineering
principles and materials to medicine and healthcare (e.g., diag-
nostic or therapeutic). It entered the general public conscience
though the proliferation of implantable medical devices, such
as pacemakers and artiﬁcial hips, to more futuristic technologies
such as somatic cell engineering and therefore the 3-D printing of
biological organs. Biomedical engineering includes the following:
1. The obtaining of new information and comprehension of living
frameworks through the inventive and substantive utilization
of experimental and analytical methods dependent on the en-
gineering sciences
2. The improvement of new devices, calculations, procedures,
and frameworks that advance science and medication and
improve clinical practice and health care
Biomedical engineering has developed throughout the years
because of headways in science and technology. As clinical prac-
tice turns out to be technological based, a dynamic move is
happening in industry to fulﬁll the need. Advancements in sci-
ence and engineering are progressively being coordinated away
from traditional technologies toward those required for medici-
nal services.
Deep learning (DL), a subset of artiﬁcial intelligence (AI), uses
a progressive degree of artiﬁcial neural networks (ANNs),
which empowers machines to process information with a
nonlinear approach. Thereafter, DL has been applied in various
scope of ﬁelds including automatic speech recognition, image

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