LUNG DISORDERS DETECTION BASED ON IRISES IMAGE USING COMPUTATIONAL INTELLIGENT ART

Abstract

Iridology as a way of revealing human organs and tissues conditions is done by iridologist by taking the image of both irises of the patients. This can be done by using a digital camera and observe each iris on the LCD display or connect the camera to a computer or a television set and observe it through the display. Research on computerized iridology has been performed before by using artificial neural network of back propagation, which is a kind of supervised learning algorithm, as the classifier [13]. Such system should be able to retain its stability while still being plastic enough to adapt to arbitrarily input patterns. Adaptive Resonance Theory (ART), another kind of artificial neural network which uses unsupervised learning algorithm, has some important traits, such as real-time learning, self-stabilizing memory in response to arbitrarily many input patterns, and fast adaptive search for best match of input-to-stored patterns [9]. That way, ART architecture is expected to be the best stable and adaptable solution in changing environment of pattern recognition. In this research, the lung disorders detection is simply designed through the steps of segmentation, extraction of color variations, transformation of lung and pleura representation area in iris image to binary form as the input of ART 1, and pattern recognition by ART 1 neural network architecture. With 32 samples and 4 nodes of output layer of ART1, the system is able to determine the existences of the four stadiums of lung disorders (acute, subacute, chronic and degenerative) in relatively short time process (approximately 1.8 to 3.2 seconds) with the accuracy of stadium recognition 91.40625% by applying the vigilance parameter value of 0.4.

Keywords: iridology, lung, pleura, segmentation, ART 1 neural network