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A new algorithm for neural network pruning is presented. Using this algorithm, networks with small number of connections and high accuracy rates for breast cancer diagnosis are obtained. We will then describe how rules can be extracted from a pruned network by considering only a finite number of...
Despite the strong theoretical foundation the Bayesian probabilistic approach to model uncertainty in medicine meets many difficulties at the implementation step. One of these difficulties is related to a large amount of conditional probabilities to be assessed and in many cases this task was...
Analyzing for abnormalities of cell images in the cervix uteri provides a basis for reducing deaths and morbidity from cervical cancer through detection of potentially cancerous cells, provision of prompt advice and opportunities for follow-up and treatments. However, cytopathology is usually...
Clinical diagnosis in acute abdominal pain is still a major problem. Computer-aided diagnosis offers some help; however, existing systems still produce high error rates. We therefore tested machine learning techniques in order to improve standard statistical systems. The investigation was based...
This paper describes how ultrasonic images of the female breast have been processed and neural nets used to aid the identification of malignant and benign areas in them. The images are windowed, filtered and pre-processed into suitable patterns for processing by a neural net. Two networks are...
Medical expert systems are a successful field of applied artificial intelligence. We constructed an otoneurological expert system in our previous research, and in this study we consider its reasoning method. The reasoning process can be described as a modified nearest neighbour solution derived...
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