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The word stroke (Cerebrovascular accident attack) is used to refer to a clinical syndrome, of presumed vascular origin, typified by rapidly developing signs of focal or global disturbance of cerebral functions lasting more than 24 h. or leading to death.
Usually, when there is an attack, the chances of a successful treatment depend essentially on the early diagnosis. In the early stage, many cases of stroke are curable if properly managed. In case of false or wrong diagnosis, precious times are lost, waste of scarce health and social services resources, the chances of curing/recovering from the attack diminishes and may eventually lead to death of patient. Stroke which has been categorized Mosalov et al., (2007) as ischemic stroke, hemorrhagic stroke and subarachnoid hemorrhage and has varying medical treatment in each case. In practice the part of medical errors while diagnosing a stroke type comes to 20-45% even for experienced doctors. According to the discussion in Jehangir and Rehman (2005), differentiation of cerebral infarction and cerebral hemorrhage is the most important first step in the management of acute stroke as clinical management of the two disorders differs substantially. In most developed countries, diagnosis is easily obtained by CT scanning, which allows the accurate distinction of hemorrhagic and ischemic types. However, quick access to CT scanning is not available in every country and hospitals. It is well known that some clinical data may suggest a hemorrhagic or ischemic stroke even though no data are specific enough to allow a reliable diagnosis.
A number of scoring systems based on clinical data determining the relative likelihood of infarction or hemorrhage were developed and tested over the last decade. Although the clinical diagnoses made using these scores seem more accurate than those made by physicians, they present several problems.
The scope of methods of neurovisualization at stroke diagnosis is limited. Thus, the development of intellectual computer support systems that could assist to decrease the part of medical errors. In this study, attempt is made at looking at the application of Neural Networks for classification of Cerebrovascular Accident Attack (stroke) in patient. A Neural Network has the ability to mimic this type of decision-making process and use a knowledge base of information and a training set of practice cases, to learn to diagnose diseases.
The behavior of an artificial neural is inspired by the assumed behavior of a real neural in organic networks (Veelenturf, 1995). A simplified model of real neuron is composed of a cell body or soma, a set of fibers entering the cell body, called the dendrites and one special fiber leaving the soma, called the axon. The simplified model of a neuron can be simulated by an artificial neuron, Fig. 1. (Krose and Smagt, 1996; Mitchell et al., 1990.
[FIGURE 1 OMITTED]
The perceptron is the basic processing element. It has inputs that may come from the environment or may be the outputs of other perceptrons.
Associated with each input, [x.sub.j] [member of] R, I = 1,2, ... n, is a connection weight or synaptic weight [w.sub.j] [member of] R and the output, y, in the simplest case is a weighted sum of the inputs Eq. 1:
y = [n.summation over (i=1)][W.sub.i][X.sub.j](t) + [W.sub.o] (1)
[w.sub.o] is the intercept value to make the model more general; it is generally modeled as the weight coming from an extra bias unit, x, which is always +1.
MATERIALS AND METHODS
The Multi-layer perceptrons artificial neural networks with back-propagation error method are feed-forward nets with one or more layers of nodes between the input and output nodes. These additional layer contains hidden units or nodes that are not directly connected to both the input and output nodes. Multilayer perceptron with back-propagation learning is perhaps the most common paradigm for supervised neural network computing to date. This has been observed in the medical imaging area as well as many other pattern recognition areas.
ANNs can be viewed as weighted directed graphs in which artificial neurons are nodes and directed edges with weights are connections between neuron outputs and neuron inputs (Jain et al., 1996). Artificial network consists of the following as discussed in (Rumelhart and McClelland, 1986):
Processing units: Within neural systems it is useful to distinguish three types of units: input units (indicated by an index i) which receive data from outside the neural network, output units (indicated by an index o) which send data out of the neural network and hidden units (indicated by an index h) whose input and output signals remain within the neural network, (Ethem, 2004; Mesut and Bob, 2004). During operation, units can be updated either synchronously or asynchronously. (Krose and Smagt, 1996; Haykin, 1999; Jones, …