A physiological neuro fuzzy learning algorithm for medical image recognition
Kwang-Baek Kim · 1999
In the recent trend of medical image recognition research, there is a vigorous interest in applying artificial neural networks (ANNs) and fuzzy theory beyond medical image recognition. However, we may face the problems of oscillation at local minima, high price in training, misidentification to degrade the efficiency of recognition etc. as well as inability to regulate rules using respectively existing ANNs and fuzzy theory. The methods can be fused into one in order to get over the problems. We propose a physiological neuro fuzzy algorithm to improve the recognition rate of medical images. The learning algorithm in the paper is proposed to capture two aspects of the brain-its physiological neuronal structure and its function. That is, the inhibition and excitation mechanism of the synapses found in physiological studies is implemented with the cooperation of a neural network and fuzzy logic. In order to evaluate the proposed algorithm, we applied them in bronchogenic cancer cell recognition. The results of the simulation have shown that the proposed algorithm is very effective for medical image recognition and guarantee the convergence in the training phrase. The algorithm may contribute to further elaborating studies such as medical expert systems, automatic control, and machine vision in the real world.