Iris nevus diagnosis: convolutional neural network and deep belief network

Authors : Oyebade OYEDOTUN, Adnan KHASHMAN

Pages : 1106-1115

View : 17 | Download : 9

Publication Date : 0000-00-00

Article Type : Research Paper

Abstract :This work presents the diagnosis of iris nevus using a convolutional neural network insert ignore into journalissuearticles values(CNN); and deep belief network insert ignore into journalissuearticles values(DBN);. Iris nevus is a pigmented growth insert ignore into journalissuearticles values(tumor); found in the front of the eye or around the pupil. It is seen that racial and environmental factors affect the iris color insert ignore into journalissuearticles values(e.g., blue, hazel, brown); of patients; hence, pigmented growths may be masked in the eye background or iris. In this work, some image processing techniques are applied to images to reinforce areas of interests in them, after which the considered classifiers are trained. We describe the automated diagnosis of iris nevus using neural network-based systems for the classification of eye images as ``nevus affected`` and ``unaffected``. Recognition rates of 93.35% and 93.67% were achieved for the CNN and DBN, respectively. Hence, the systems described in this work can be used satisfactorily for diagnosis or to reinforce the confidence in manual-visual diagnosis by medical experts.
Keywords : Iris nevus, diagnosis, convolutional neural networks, deep belief networks