Detection of Gastric Disorders through Iris Image Using the Perceptron Artificial Neural Network Method
The human eye iris has a different pattern for each human being, so it is possible to use it as a basis for biometric recognition known as iridiology. Iridiology is the study of the iris through the shape, structure, changes in color, appearance and symbols found in the eye area. The iris is composed of a layered smooth membrane where it is connected to the nervous system of all body organs, one of which is the stomach organ. By utilizing the science of iridiology, the author wants to make a detection software for gastric disorders through iris images using the perceptron neural network method. From the iris image, a vector value will be obtained from the preprocessing process, where this value will be used for the learning process on the perceptron neural network so that the learning outcomes will be found which will be stored in the database. The training data will be compared with the data during the detection process. Of the 30 iris images tested, the system was able to recognize almost all of these images well. So that the accuracy rate of this system is 90%. Based on these results, it can be concluded that this system is able to detect gastric disorders through iris images. In making this system the author uses Visual Basic.Net programming language.
Authors: Khairuna Phonna, Zulfan Khairil Simbolon, Mahdi Mahdi
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