Page 208 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

198 Chapter 7 Early detection and diagnosis using deep learning

synthetic biology and artificial life. While good engineering advo-
cates further research and testing, several public protests take
place to challenge the ethics of this research.
3. Target sites
Despite discovering several new drugs and advancements in
medicine, getting those drugs to reach the targeted cells remains
a huge challenge. If the measures are too aggressive, there may be
serious side effects and harm the healthy cells. If the measures
are less intrusive, they may not be as effective.
4. Funding
Despite being one of the most prominent branches of studies
and research, biomedical engineering faces a serious lack of
funding. The equipment is usually quite expensive; hence, the
need for private and governmental grants is further increased.

2. Diagnostics using deep learning

Before we were using ML for diagnosis, there were big ma-
chines kept in hospitals, which were responsible for testing and
declaring if the person was suffering from a particular disease
or not. It is also known that all the people are not able to avail
the facility due to reasons such as high hospital bills, hectic life
schedule and sometimes just because the hospital is far away.
In cases like these that need diagnosis as soon as possible, just
for the sake of the lives of patients, a number of biologists, scien-
tists, pharmacists, and researchers in the field of healthcare are
working in the field of artificial intelligence (AI) and are devel-
oping different algorithms for dealing with different problems.
Many algorithms that are already developed are on cancer,
diabetes, Alzheimer' disease (AD), heart diseases, and so on. [1].
Includes huge artificial neural network layers [1] partaking
interrelated nodes, which can reorganize themselves whenever
some new data originate. This method permits the mainframes
to acquire the knowledge by their own shorn of the necessity of
human intervention. This chapter focuses on all the current
growths in this field of ML and DL that have resulted in substantial
influences in the uncovering and analysis of numerous ailments
and viruses.
Neural networks are unconventional and are advancing at an
extraordinary frequency, with an endless number of applications
controlled by this technology, which has made it an essential part
of the industry such as healthcare, financial, technical, sports, or
media [2].