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Norsarini Salim (84651)
Faculty of Information Technology
Universiti Utara Malaysia,Sintok, Kedah
Email: S84651@ss.uum.edu.my
Neural Network is very familiar to the researcher in Artificial Intelligent field. Many researchers found that artificial neural network can give many different answers in many different problem areas. The reason is that the field of neural networks is immensely diverse, drawing interest from and applications for, medicine, mathematics, computer science, chemistry, economics, and many other fields.
Artificial neural networks are designed to simulate the behavior of biological neural networks for several purposes. According to DKlerfors(1998), Artificial Neural Network is a system loosely modeled on the human brain. The field goes by many names, such as connectionism, parallel distributed processing, neuro-computing, natural intelligent systems, machine learning algorithms, and artificial neural networks. It is an attempt to simulate within specialized hardware or sophisticated software, the multiple layers of simple processing elements called neurons. Each neuron is linked to certain of its neighbors with varying coefficients of connectivity that represent the strengths of these connections. Learning is accomplished by adjusting these strengths to cause the overall network to output appropriate results.
Neural networks, with their remarkable ability to derive meaning from complicated or imprecise data, can be used to extract patterns and detect trends that are too complex to be noticed by either humans or other computer techniques.
The major problem in medical field is to diagnose disease. Human being always make mistake and because of their limitation, diagnosis would give the major issue of human expertise. One of the most important problems of medical diagnosis, in general, is the subjectivity of the specialist. It can be noted, in particular in pattern recognition activities, that the experience of the professional is closely related to the final diagnosis. This is due to the fact that the result does not depend on a systematized solution but on the interpretation of the patient's signal (Lanzarini and Giusti, 1999).
Brause(2001) highlighted that almost all the physicians are confronted during their formation by the task of learning to diagnose. Here, they have to solve the problem of deducing certain diseases or formulating a treatment based on more or less specified observations and knowledge. For this task, certain basic difficulties have to be taken into account:-
Brause(2001) also give an example of a study in the year 1971 showed these basic facts in the medical area. This study had shown that human have many limitations in diagnosis. The results of this experiment were as follows:-
From this result we can see that humans can not ad hoc analyze complex data without errors.
This paper ware discussed how Neural Network approach can diagnose disease using patient medical data such as breast cancer, heart failure, medical images, acidosis diseases, and lung cancer
Breast cancer is the second largest cause of cancer deaths among women. The automatic diagnosis of breast cancer is an important, real-world medical problem. A major class of problems in medical science involves the diagnosis of disease, based upon various tests performed upon the patient. When several tests are involved, the ultimate diagnosis may be difficult to obtain, even for a medical expert. This has given rise, over the past few decades, to computerized diagnostic tools, intended to aid the physician in making sense out of the confusion of data (Kiyan and Yildirim, 2003).
Neural network have been applied to breast cancer diagnosis. (Kiyan and Yildirim, 2003) employed Radial Basis Function, General Regression Neural Network and Probabilistic Neural Network in order to get the suitable result. From overall results, it is seen that the most suitable neural network model for classifying Wisconsin Breast Cancer data is General Regression Neural Network. This work also indicates that statistical neural networks can be effectively used for breast cancer diagnosis to help oncologists.
Making prognosis for patients with congestive heart failure is difficult due to the complex nature of this multisystem disease. No single criterion helps to identify patients at risk, and a combination of several prognostic parameters is recommended (Cowburn et al., 1998). Neural networks are associative selflearning techniques with the ability to identify multidimensional relationships and perform pattern recognition in non-linear domains. Atieza et al., (2003) identified that classification is the best result for this cases.
Neural networks are extremely useful, since not only are they capable of recognizing patterns with the aid of the expert, but also of generalizing the information contained in the input data, thus showing relations which are a priori complex. (Laura and Armando, 1999) combined the processing of digital image and neural network to carry out the required recognition and classification. As a result, the solution to the problem can be divided in two parts: the segmentation of different elements, and their subsequent classification. In this case good results good results have been obtained thanks to the definition of a new clustering algorithm based on e re-definition of the input image. As for the classification stage, different solutions using neural networks have been compared, the results obtained being correct, with an error smaller than 10%.
Neural Networks are used in pattern recognition because of their ability to learn and to store knowledge. Because of their 'parallel' nature can achieve, Neural network can achieve very high computation rates which is vital in application like telemedicine (Siganos, 1995).
Ultsch et al. (1995) used the capability of neural network to diagnose acidosis diseases by using knowledge based system in their hybrid system. The data set consists of 11 attributes originating from the blood analysis. Several classification methods according to (Deichsel and Trampisch, 1985) were used to explain these data. The Neural Network together with the UMatrix method was able to classify the data into the subcategories healthy, lacacidemia, metabolical acidosis, respiratory acidosis and one patient with cerebral deficiency. They used rule generation module to extracted rules out of the Neural Network, which were described by 4 or 5 attributes resembling more closely the decisions made by medical experts (Ultsch and Li, 1993).
Lung cancer is another diseases that commonly known as a deadly disease in the world. Many patients suffer from this disease. Early detection of this disease is very important to prevent this disease. Expertise have to measures for early stage lung cancer diagnosis mainly includes those utilizing X-ray chest films, CT, MRI, isotope, bronchoscopy, andneedle biopsies. According to Zhou et al. (2001) at present, the specimens of needle biopsies are usually analyzed by experienced pathologists. Since senior pathologists are rare, reliable pathological diagnosis is not always available.
Zhou et al. (2001) named his an automatic pathological diagnosis procedure named Neural Ensemble based Detection (NED). It is proposed and realized in an early stage Lung Cancer Diagnosis System (LCDS). NED utilizes an artificial neural network ensemble to identify cancer cells in the images of the specimens of needle biopsies obtained from the bodies of the subjects to be diagnosed. A fast adaptive neural network classifier has been used to identify the lung cancer cell. Zhou et al. (2001) stated that a fast adaptive neural classifier that performs one-pass incremental learning with fast speed and high accuracy and does not require the user manually set up the number of hidden units.
Neural network has been proven of their capabilities in many domains such as medical application. Neural network with ability to learn by example makes them very flexible and powerful in medical diagnosis. Neural network show that experience from expertise is not enough in diagnosis. Nowadays, physicians combined this opportunity that give by neural network and their expertise to detect early stage of patients disease.
Atieza, F., Alzamora, N.M., Velasco, J.A.D., Dreiseitl, S., and Ohno-Machado, L. (2003). Risk Stratification in Heart Failure Using Artificial Neural Networks. Valencia, Spain.
Brause, R.W. (2001). Medical Analysis and Diagnosis by Neural networks. Computer Science Department, Franfurt a.M., Germany.
Cowburn, P.J., Cleland, J.G.F., Coats, A.J.S., and Komajda, M. (1996) Risk stratification in chronic heart failure. Eur Heart J ;19:696-710.
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Laura, L., and Armando, D. G. (1999). Pattern Recognition in Medical Images Using Neural Networks. Universidad Nacional de La Plata.
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Submitted: 25/09/2004