A powerpoint presentation on Artificial Neural Networks

1. A presentation on
Compiled by :
Tonmoy Bhagawati,
DC2013MTC0033
Mtech 1st Semester,DBCET
Specialization : Artificial
Intelligence

2. Networks : An Introduction
One efficient
way of solving
complex
problems is
following the
lemma
“divide and
conquer”
Networks are
one approach
for achieving
this. All
networks are
characterized
by the
following
components:
a set of nodes,
and
connections
between
nodes.
The
connections
determine the
information
flow between
nodes. They
can be
unidirectional
and
bidirectional
The
interactions
of nodes
though the
connections
lead to a
global
behaviour of
the network.
This global
behaviour is
said to be
emergent.
One type of
network sees
the nodes as
‘artificial
neurons’.
These are
called
Artificial
neural
networks
(ANNs).

3. Biological Neural Networks
Natural neurons receive signals through
synapses located on the dendrites or
membrane of the neuron.
When the signals received are strong enough
(surpass a certain threshold), the neuron is
activated and emits a signal though the axon.
This signal might be sent to another synapse,
and might activate other neurons.

4. The complexity of real
neurons is highly
abstracted when modeling
artificial neurons.
These basically consist of inputs (like
synapses), which are multiplied by
weights (strength of the respective
signals), and then computed by a
mathematical function which
determines the activation of the neuron.
Another function (which
may be the identity)
computes the output of the
artificial neuron (sometimes
in dependence of a certain
threshold).
Artificial Neural Networks

6. The higher a weight of an artificial neuron
is, the stronger the input which is
multiplied by it will be.
Weights can also be negative, so we can say
that the signal is inhibited by the negative
weight. Depending on the weights, the
computation of the neuron will be different.
By adjusting the weights of an artificial
neuron we can obtain the output we want
for specific inputs. But when we have an
ANN of hundreds or thousands of neurons,
it would be quite complicated to find by
hand all the necessary weights.
we can find algorithms which can adjust
the weights of the ANN in order to obtain
the desired output from the network.
Learning or
Training

7. ANN and the Medical Science
Experience is as important
for an ANN as it is for man.
Treatment planning in
medicine, radiotherapy,
rehabilitation, etc. is being
done using ANN.
Mortality prediction by
ANN in different medical
situations can be very
helpful for hospital
management.
ANN has a promising future
in fundamental medical and
pharmaceutical research,
medical education and
surgical robotics.
Robustness and the ability
to filter out noise is very
desirable for diagnostic
purposes where much
unnecessary data need to
be filtered out

8. Specifics
Clinical diagnosis:
Acute myocardial infarction (AMI)
was one of the earliest applications
of ANNs
Pulmonary embolism (PE) and back
pain are two other areas where
comparisons have been made
between diagnostic efficiencies of
human experts and ANN.
Pathology : picture processing
ability of ANN makes it very suitable
for use in classification of
histology/cytology specimens.
Microbiology :
Paralysis mass spectrometry (PMS) is
a specialized area of microbiology in
which the potential of ANN has been
demonstrated.
Outcome prediction of a patient's
clinical condition/profile, his stay in
the hospital, etc. are becoming
increasingly important and neural
networks have been used for this
purpose.
Treatment : Effective management
of any disease depends on a number
of factors: correct diagnosis, choice
of treatment and monitoring of the
patient's condition during and after
treatment. Each factor, again, is
controlled by many parameters.

9. Advantages and Drawbacks
Noise-Tolerance, Fault-
Tolerance against Hardware
errors, Sensible classification
of unknown input, Building
own internal representation
Local Minima, Slow Training
Process, Choice of suitable
network topology,
Preprocessing

10. Conclusion
Artificial neural network theory is derived from many disciplines including neuroscience,
psychology, mathematics, physics, engineering, computer science, philosophy, biology and linguistics.
ANNs exploit the massively parallel local processing and distributed representation properties that are
believed to exist in the brain. The primary intent of ANNs is to explore and reproduce human information
processing tasks such as speech, vision, knowledge processing, motor control and especially, pattern
matching.
Though ANN is being tested in various fields of medicine, there remains a lot of room for its improvement
and validation.
An extensive amount of information is currently available to clinical specialists, ranging from
details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each
type of data provides information that must be evaluated and assigned to a particular pathology during
the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis,
artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can
be employed. These adaptive learning algorithms can handle diverse types of medical data and integrate
them into categorized outputs. In this presentation, we briefly reviewed and discuss the philosophy,
capabilities, and limitations of artificial neural networks in medical diagnosis through selected subjects.

11. References
1. Importance of Artificial Neural Network in Medical Diagnosis disease like acute nephritis disease and heart
disease by Irfan Y. Khan, P.H. Zope, S.R. Suralkar Dept. of Ele. & Tele. SSBT’s college of Engg. & Tech,
Bambhori, Jalgaon, India
2. Artificial neural network and medicine by Zulfuquar Hossain khan, Saroj Kant Mohapatra, Prafulla kumar
Khodiar and S. N. Ragu Kumar. (1997)
3. Baxt WG. Use of an artificial neural network for the diagnosis of myocardial infarction. Ann Intern Med
1991
4. Artificial Intelligence : a Modern Approach 2nd edition by Russell and Norvig
5. Artificial Neural networks by b. Yegnanarayana
6. Artificial Neural Networks for Beginners by Carlos Gershenson
7. Artificial neural networks- the hot topic in recent pharmaceutical research by Abdul Althafi
8. Artificial Intelligence and Intelligent Systems by N.P. Padhy
9. Artificial neural networks Opening the black box by Judith E. Dayhoff and James M. DeLeo
10. Application of Neural Networks in Diagnosing Cancer Disease Using Demographic Data- 2010
International Journal of Computer Applications (0975 – 8887) Volume 1 – No. 26
11. http://en.wikipedia.org/wiki/Artificial_neural_network (accessed o n 11.12.13)