A presentation onArtificial Neural Networks with special reference to the domain

of Medical Science

Compiled by :Tonmoy Bhagawati, DC2013MTC0033

Mtech 1st Semester,DBCETSpecialization : Artificial

Intelligence

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 unidirectiona

l 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).

Biological Neural NetworksNatural 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.

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.

Artificial Neural Networks

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

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.

SpecificsClinical 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.

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

ConclusionArtificial 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.

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