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Artificial Neural Networks In

Medical Diagnosis

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Definition

Artificial Neural network is an information

processing system composed of highly

interconnected processing elements

(neurons) working in union to solve

specific problems.

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The general structure of artificial neural

networks consists of neurons interconnected to

form three types of layers:

Input Layer (Receives input from outer world)

One or more Hidden Layers (Process input)

Output Layer (Gives output to outer world)

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Biological Inspiration

ANN is Highly interconnected set ofneurons.

Inspired from biological neurons.

Output is produced by applying activationfunction on the input.

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Activation functions

Linear Function

Logistic Function

Linear (x) = x

Logistic (x) = 1/(1+exp(-x))

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Sign Function

Step (x) = 1 if x >= t, else 0

Step Function

Sign (x) = +1 if x >= 0, else -1

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Transformation

How input is transformed to outputs:

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Architecture

Neurons are linked together in layers to

form a specific architecture.

Transforms inputs into meaningful outputs.

Artificial Neural network can be broadly of

two types:

Feed Forward Architecture

Recurrent Architecture

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Feed Forward Neural Networks

The information ispropagated from theinputs to the outputs

Computations of Non

linear functions from ninput variables.

There is NO cyclebetween outputs andinputs.

x1 x2 xn..

1st hidden

layer

2nd hidden

layer

Output layer

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Recurrent Neural Networks

Can have arbitrarytopologies.

Training is more

difficult. There can be cycles

between outputs andinputs.

x1 x2

1

0

10

1

0

0

0

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Training

Every neuron in ANN has a weight

associated with it.

Training takes place by giving different

instance inputs.

Weights are adjusted until the desired

results are got as output.

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Training can be performed in two modes: Supervised

Unsupervised

Some training algorithms of common useare: BPN, MLP, RBF, LVQ etc.

Training is continued until a desired

accuracy is achieved or error comes to aminimum level.

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Neural Networks in Medical Field

Disease Diagnosis

Prediction of chances of a disease

Robotic Handling of Surgery Measuring Effect of new Medicines

Continuous Patient Monitoring

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Collect Samples

Take both normal and abnormal

samples of images or data from tests.

More the number of samples, more will

be accuracy.

Type of image depends upon the nature

of research.

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Preprocessing

Preprocessing is necessary for renderingthe image fit for analysis.

If one is using data then image processing

steps are not required. Various methods used by different

researchers are:

Enhancement Filtering

Compression etc..

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Extract Features

Extract features of the image that are

necessary for diagnosis.

Difference between left and right eye

features will be used for diagnosis.

In case of data, important parameters are

selected.

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Normalize

A data set is created by normalizing thefeatures and actual diagnosis results.

S. No. Features Result

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Training And Testing Set

Dataset can be randomly divided into

mutually exclusive training and testing

sets.

Training setis usually larger than the

testing set.

Size depends on number of available

samples.

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K-Fold Cross Validation

Another method for selecting training and

testing sets.

Samples are divided into K mutually

exclusive subsets of equal size.

K-1 subsets are used for training and one

subset is used for testing.

The procedure is repeated K times taking

different subset for testing.

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Design of Neural Network

Number ofinputs and outputs.

Number ofhidden layers.

Number ofneurons in each hidden layer. Interconnections between different

layers.

Training algorithm to be used. Initial weights.

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Training and Testing

Input the training set along with the

results.

Training is performed until weights

continue to change.

After training, test set is introduced but

the results are not fed along with.

Now the neural network has to perform

similar operations and produce diagnosis.

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System Evaluation

Methods such as confusion matrix can

be used to evaluate system performance.

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Evaluation Parameters

Sensitivity=

Specificity=

Accuracy=

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